You are also welcome to complete the FREE MEMBERSHIP FORM for Technical Committee on Learning Technology. Please complete the form at: http://lttf.ieee.org/join.htm.

Besides, if you are involved in research and/or implementation of any aspect of advanced learning technologies, I invite you to contribute your own work in progress, project reports, case studies, and events announcements in this newsletter. For more details, please refer author guidelines at http://lttf.ieee.org/learn_tech/authors.html.

Kinshuk
Editor,
Learning Technology Newsletter
kinshuk@ieee.org

5P^th^P IEEE International Conference on Advanced Learning Technologies (ICALT 2005)

July 5-8, 2005

Kaohsiung, Taiwan

HThttp://lttf.ieee.org/icalt2005/TH

· Important Dates

February 4, 2004 - paper submission

March 25, 2005 - notification of acceptance

April 15, 2005 - Copyright form submission

April 15, 2005 - final camera-ready manuscript

April 29, 2005 - author registration deadline

** Proceedings will be published by the IEEE Computer Society Press. **

Theme: "Next generation e-learning technology: intelligent applications and smart design"

The conference will bring together people who are working on the design, development, use and evaluation of technologies that will be the foundation of the next generation of e-learning systems and technology-enhanced learning environments.

We invite submission of papers reporting original academic or industrial research in the area of Advanced Learning Technologies. All papers will be peer-reviewed. Complete papers will be required for review process; only abstracts will not be sufficient.

All authors of accepted submissions will be required to complete IEEE Copyright Form.

Authors of selected papers will be invited to submit extended versions for a Special Issue of the Journal of Educational Technology & Society (ISSN 1436-4522).

· Topics of Interest

Adaptivity in Learning Systems

Advanced uses of Multimedia and Hypermedia

Ambient Intelligence and Ubiquitous learning

Application of Artificial Intelligence Tools in Learning

Architecture of Context Aware Learning Technology Systems

Artificial Intelligence Tools for Contextual Learning

Building Learning Communities

Computer Supported Collaborative Learning

Distance Learning

e-Learning for All: Accessibility Issues

Educational Modelling Languages

Evaluation of Learning Technology Systems

Information Retrieval and Visualization Methods for Learning

Instructional Design Theories

Integrated Learning Environments

Interactive Simulations

Knowledge Testing and Evaluation

Life-Long Learning Paradigms

Learning Objects for Personalised Learning

Learning Styles

Media for Learning in Multicultural Settings

Metadata for Learning Resources

Mobile Learning Applications

Participatory Simulations

Pedagogical and Organisational Frameworks

Peer-to-Peer Learning Applications

Practical Uses of Authoring Tools

Robots and Artefacts in Education

Simulation-supported Learning and Instruction

Socially Intelligent Agents

Speech and (Natural) Language Learning

Learning Objects for Personalised Learning

Teaching/Learning Strategies

Technology-facilitated Learning in Complex Domains

Virtual Reality

Virtual Spaces for Learning Communities

· Program Co-Chairs

Demetrios G Sampson, University of Piraeus and Center for Research and Technology - Hellas, Greece

Peter Goodyear, University of Sydney, Australia

· General Co-Chairs

Kinshuk, Massey University, New Zealand

David Jin-Tan Yang, National Kaohsiung Normal University, Taiwan

Toshio Okamoto, The University of Electro-Communications, Japan

· Workshop Chair

Roger Hartley, University of Leeds, United Kingdom

· Tutorial Chair

Nian-Shing Chen, National Sun Yat-sen University, Taiwan

· Paper Submissions

Submissions are invited in the following categories:

- Full paper: 5 pages

- Short paper: 3 pages

- Posters: 2 pages

- Tutorial proposals: 2 pages

- Panel proposals: 2 pages

- Workshop: 2 pages

Submission information is available at:

http://lttf.ieee.org/icalt2005/papers.html

· For general information, please contact:

Dr. Maiga Chang

Office of e-Learning National Project

P.O. Box 12-162, Chung-Li, 320, Taiwan

Tel: +886 3 4227151-ext 4750

Fax: +886 3 4260120

maiga@ms2.hinet.net

T
TStructured, Online Evaluations and the First-Year Design Process

A key component to the first-year engineering curriculum at DrexelUniversity is an interdisciplinary design project. Students receive credit for both design and English courses in the winter and spring terms of their freshman year. The 600 or so first-year engineers organize design teams, seek guidance from engineering faculty to define design problems, and work with both engineering and English faculty to develop three written documents and an oral presentation.

The projects are often extensive and span the winter and spring terms (Drexel runs on trimesters). Each team has five members and works with an engineering advisor and two English advisors to create a final report, which runs upwards of 20 pages, plus appendices, and includes a formal table of contents, abstract, executive summary, and report body.

This complex undertaking is facilitated by a novel Web-based software tool that allows faculty members to access a database of comments and electronically track teams of five students through the project deliverables: a Problem Definition Statement, Proposal, Oral Presentation, and Final Report. Faculty use the shared database to generate and track both qualitative and quantitative feedback.

Because the students often have different English faculty, two English teachers grade each of the deliverables to ensure consistency and fairness. The English grade reflects specific course objectives that differ in emphasis from those used by the individual engineering advisors. For instance, the English faculty stress the conventions of language, clarity, and conciseness while the Engineering faculty focus more on content and design. It is extremely important to make the language and tone of feedback consistent. The engineering advisor grades the project for a course called Engineering Design and Laboratory, while the two English advisors grade the project for the freshman humanities sequence of courses which includes a technical writing component.

Figure 1. Excerpt of the Evaluation used for the Final Report

The design project is a hallmark of the Drexel Engineering Curriculum (tDEC) which evolved from the 1988 curricular experimental project entitled "An Enhanced Educational Experience for Engineering Students" that was funded in part by a grant from the Engineering Directorate of the National Science Foundation.

Responding to student concerns about contradictory feedback from the two English advisors and variation in feedback among the 10 to 12 faculty involved, the English faculty sought a tool to help facilitate the assessment and response process.

In 2003 English faculty members began using an online tool, waypoint (www.gowaypoint.com), to evaluate design deliverables. Students still submit hard copies of their papers and faculty still make comments in the margins of the paper. But now faculty utilize waypoint to write the “end comments” - —really the crucial part of the student-teacher interaction that guides the revision process

Before waypoint, students received two sets of feedback, one from each English advisor,. Now, student teams receive a single, unified feedback memo that has been agreed upon by both humanities advisors. Waypoint allows the faculty to decide, in advance, on specific criteria for each assignment and then evaluate student work consistently. Students received an email evaluation that describes, in great detail, the advisors’ joint feedback.

Figure 1 shows an excerpt of the evaluation page generated for the Final Report. Every “Element” (in waypoint, an element is simply a criterion used to evaluate writing) was custom generated by these particular faculty to suit the assignment. After deciding on the criteria, faculty can prewrite the basic structure of the comments they would make to students for each performance level of a given element, thus anticipating the kind of work students are known to do. When evaluating, English faculty simply click on the choice that is most applicable and work their way through the evaluation. It is crucial to note that the prewritten text can be modified (see Figure 2) at any step, providing students with advice specific to their report. Since waypoint is Web-based, the two advisors can easily collaborate on the feedback and grade at every step in the process.

Figure 2. Example of Choosing and Modifying Prewritten Text

English faculty can email or print these clear, detailed response memos back to the first year design team members (see Figure 3 for an excerpt of such feedback).

Figure 3. Excerpt from the Feedback Generated for a Design Team

Because every choice reflects a quantitative assessment (if the second of four choices is chosen, the database records the data point of 2/4) data is easily generated to identify strengths and weaknesses across the entire cohort. For instance, all 110 design teams could be easily analyzed against basic core competencies like research (see Figure 4).

Figure 4. Team performance for the research competency, February to June 2004

(1 is exemplary, 4 unacceptable)

Also, because all responses are stored in a database, accessing comments written earlier in the process is easy, allowing everyone involved to see how a particular team has progressed in a particular communication competency. See Figure 5 for an example of the history of comments.

Figure 5. Example of Comment History Accessed During the Evaluation Process

The response from students and faculty has been extremely positive. Students appreciate the detailed feedback they receive and are more motivated to read and understand the commentary. The time to evaluate design reports has been reduced, in some cases by up to 50%, and concerns about contradictory advice have been eliminated. Since each faculty member might be responsible for reading and evaluating 40 design reports, even a 15% reduction in time is a significant achievement. In addition, all the feedback given to design teams is readily available – an ideal resource for analyzing pedagogy and researching the effectiveness of curriculum design.

Scott J. Warnock

Assistant Professor of English

Drexel University, USA

HScott.Warnock@drexel.eduH

Andrew J. McCann

Visiting Professor of English

Drexel University, USA

mccann@drexel.edu

Meta-analysis of Technology and Privacy in an Advanced Business Course

The Project

BCOR 4000-006 is a required core curriculum course in the Systems Management major for fourth year students obtaining a Bachelor’s Degree in business from the Leeds College of Business at the University of Colorado at Boulder. Technology and Society: Privacy for Fun and Profit requires students to closely examine issues of privacy enabled or caused by technology. Furthermore, the course requires students to use various technologies to study and report on a wide range of topics, which particularly focus on the unprecedented aggregation of personal information and the speed of collection, which cannot be matched by social norms or laws. Students dissect the technologies, social trends, and the business and governmental uses of personal information to reap profits or manage society.

The major project of the semester requires student dyads to examine privacy implications of technologies, e.g., microchips embedded in humans, national ID cards, biometrics, and overseas outsourcing of banking and medical records. Each pair chose a topic and is currently engaged in writing a well researched paper of approximately 5000 words on that topic; students collaborate in their research, design, writing and revising/editing online and face to face. The teams present their work orally and in writing to the class in progressive stages: questions and proposal, initial report, interim report, and final report. Students use tools ranging from computers and software, to overhead projectors, to video, CD, DVD, to cell phones, and more to demonstrate the implications of the technology and to provide visual images of the processes and ethics surrounding privacy issues. The final student product will be a book containing all student projects. Each student will receive a copy; additional copies will be used as a course text in future terms.

Students work on committees, of four to six members, that are providing book production processes: editing and revising, determining a budget, obtaining and selecting bids for printing, and publishing the book complete with copyright and ISBN. Primarily systems management majors, the students have found their previous studies in marketing, finance and other fields have many applications to this project.

Several guest speakers also participate: a senior instructor in writing and rhetoric presented conceptual and practical aspects of the writing project, and consults with the editorial and publication teams to polish and complete the manuscript; the CEO of a local information technology firm spoke on eXtreme data warehousing and ethics; an expert presented survey design; and a business librarian discussed library resources and research.

Learning Goals

The course actively engages students in all aspects of information technology, using it, studying it, recognizing the ethical implications of its ability to collect and analyze data, and meta-analyzing both the technology and the students’ understandings and reactions to it, and it provides rich experiential learning for these students, many of whom are already employed in systems management. The primary goals for the course, to learn about, appreciate, and understand the privacy implications of information technology in the hands of business and government, are being well met in the class. The students have a vested interest in their topics, e.g., one young man with serious medical problems has found his medical records stored digitally in India, where few privacy regulations exist.

Interesting Findings

One pair of students presented their interim project report about opinions on the Patriot Act and the loss of privacy for individuals should the government determine that they are potential terrorists. They downloaded video clips of the Bush campaign along with clips from Fahrenheit 9/11’s treatment of the Act. In their presentation, they proceeded to show images and audio without demonstrating the implications of downloading the video from Kazaa before it was officially released. The professor and class engaged in a lively analytical discussion of privacy violations, piracy, and ethics.

Thus, the course encourages meta-analysis of an original analysis, one of the most constructive forms of critical thinking that can be taught at the university level. And, in this case, the students had employed advanced technologies, used for teaching and learning as well as for information gathering and entertainment, both as the subject of their thinking as well the tools for analysis. This is, of course, a second meta-process: using the tools to examine the uses of the tools.

A Survey

In addition to their online and library research on privacy, the students are surveying approximately 640 fellow students, to examine the ways in which they use technology, their attitudes toward technology used in learning, and to learn about their attitudes toward privacy with regard to technology. The survey will be distributed approximately ¾ of the way through the course, and should provide useful data for analysis. Examining student attitudes, particularly toward the ways in which technology interfaces with their privacy, is important. Students at a large university are a microcosm of society; they use technology for nearly every possible purpose—and they are “up” on the latest uses in of electronic business communications (credit cards, bank cards, ID cards, etc.). They meet socially in chat rooms, explore the unknown in gaming, and communicate electronically perhaps more frequently than in person. As the survey seeks to understand how students use and learn while using technology, it more importantly seeks to discover whether or not students consider the ethical and social implications of that use.

Conclusion and Implications

This project of examining privacy issues and ethics related to technology involves students who will be working in systems management. The research, writing, oral presentations, and publishing of the students’ projects involve many uses of technology as well as the students’ meta-analysis of these technologies. Furthermore, the course encourages meta-analysis of the privacy issues, ethics, and social implications involved with the students’ own work. This course, already half completed, demonstrates that using advanced technologies for teaching and learning enrich the educational experiences of upper division students. More such projects in related courses should be considered, for these applications of learning technologies as well as advanced critical thinking skills will serve the students well in their future careers.

Two references of interest, as used in class:

Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information Privacy: Measuring Individuals' Concerns about Organizational Practices. MIS Quarterly, 20 (2), 167-196. [Their instrument in addition to self-developed items is used for the class survey]

Mason, R. O. (1986). Four Ethical Issues of the Information Age. MIS Quarterly, 10 (1), 5-12. [The class uses this reference to examine the four main ethical concerns of the information age; see http://www.misq.org/archivist/vol/no10/issue1/vol10no1mason.html]

Anne Bliss, Ph.D.

Senior Instructor

Program for Writing and Rhetoric

317 UCB -- ENVD 1B27D

University of Colorado at Boulder

Boulder, CO80309-0317USA

HAnne.Bliss@colorado.eduH

Kai R.T. Larsen, Ph.D.

LeedsCollege of Business

University of Colorado at Boulder, USA

An Examination of a Comprehensive Preschool Curriculum and its Impact on Early Literacy Skills in Newark, New Jersey

Abstract

In this independent 2003-2004 study of high-poverty preschools in Newark, New Jersey (This study was independently conducted. The school district represented in this study is not associated with, affiliated with, or endorsed by LeapFrog Enterprises, Inc.), use of the LeapFrog SchoolHouse Ready, Set, Leap! comprehensive and technologically-based curriculum significantly enhanced student performance. Classrooms using the RSL curriculum with a high level of program implementation showed significantly higher scores on tests of blending, initial sound identification, and rhyming than did control classrooms. These findings suggest that explicit, thematic instruction is effective in instructing critical phonemic-based skills, thus positioning preschool children to become successful, proficient readers.

Introduction

Preschool programs are increasingly expected to provide explicit instruction in early literacy skills, which have been linked to successful reading (NICHD, 2000). Research suggests that quality of instruction can further impact performance on outcome measures. The Ready, Set, Leap! (“RSL”) comprehensive and technologically-based curriculum was developed by LeapFrog SchoolHouse (http://www.leapfrogschoolhouse.com) to provide explicit instruction in crucial early reading skills.The Ready, Set, Leap!™ program is a research-based, PreK integrated curriculum that is correlated to state standards.

Award-winning multi-sensory and portable technology is infused into each lesson to engage students and address the needs of all learners. The LeapPad personal learning tool, the LeapMat learning surface and the LeapDesk workstation (all elements of RSL) each use technology to bring the curriculum to life.

Ø The LeapMat Learning Surface is an electronic device that uses touch, sight, and sound to teach letter-name recognition, letter-sound association, and spelling of three-letter words.

Ø The LeapPad technology is an interactive, multisensory personal learning tools which provides students with engaging instruction and immediate feedback, appealing to all the ways students learn: it enables students to enjoy stories independently, while developing essential vocabulary and pre-reading skills; helps students develop phonological awareness through hearing sounds, words, and sentences spoken fluently; and provides unlimited repetition and practice.

Ø The LeapDesk technology delivers instruction in letter names and sounds, phonemic awareness, and concept of word. It also motivates students with high interactivity and immediate audio feedback; enables teachers to assess early literacy skills for the individuals and the entire class; and allows students to practice tracing letters on a lighted writing pad. Through the LeapDesk assessments, teachers can track student progress over time; and the LeapDesk prescribes lessons to each child depending on their individual needs.

This study, conducted by RMC Research Corporation (“RMC”) (http://www.rmcres.com) with funding from Leapfrog Enterprises, Inc., assessed the effectiveness of Ready, Set, Leap! among public preschools in Newark, New Jersey.

Sample

A total of seventeen high-poverty, inner-city Newark public schools participated, and were randomly assigned to treatment or control groups. Classrooms of students with disabilities were excluded. Twenty-seven classrooms were included in this analysis; pre- and post-test scores for 254 students were analyzed. Groups were matched on age, English-language proficiency, free/reduced lunch, and gender. There was a statistically significant difference in ethnicity (Table 1); more minorities in the treatment than control group.

Table 1. Student Demographic Data
	Control (n =125)				RSL (n = 129)				FB_(1,253)_B	cP²^P(df)
Variables	Mean	(SD)	n	%	Mean	(SD)	n	%
Age	4.53	(0.30)			4.50	(0.31)			.85
ELL			14	11			20	16		1.01(1)
Free-Reduced Lunch			100	80			112	87		2.14(1)
Ethnicity										24.60(3)	^P***^P
Caucasian			30	24			6	5
African-Am.			46	37			66	51
Hispanic			40	32			54	42
Asian-Am & Other			9	7			3	2
Gender
Female			66	53			73	57		0.34(1)
Male			59	47			56	43

*** p < .000

Method

Students were pre-tested in October 2002 and post-tested in May 2003. The assessment battery included:

Ø DIBELS Initial Sound Fluency (Good & Kaminski, 2002)

Ø DIBELS Letter Naming Fluency

Ø Woodcock-Johnson III Letter-Word Identification (Woodcock, McGrew, Mather & Schrank, 2001)

Ø Woodcock-Johnson III Passage Comprehension

Ø Woodcock-Johnson III Sound Awareness-Rhyming

Ø Comprehensive Test of Phonological Processing (“CTOPP”) Blending Words (Wagner, Torgesen & Rashotte, 1999)

Ø Peabody Picture Vocabulary Test III (“PPVT”) (Dunn & Dunn, 1997)

Measures were individually administered in two sessions to avoid fatigue; the order of the tests was randomized. Both groups were matched at pre-test on all measures of early literacy skills.

Treatment

Control group teachers used only the High Scope framework, which allows children to control their individual learning. Treatment teachers integrated the RSL curriculum into the High Scope framework, conducting individualized assessments to assess students’ learning needs.

Result

Main Treatment Effect

To determine whether use of the RSL program resulted in enhanced academic gains while controlling for differences in pre-test scores, a two-level hierarchical linear model (“HLM”) technique was employed, with students nested within teacher and the treatment effect tested at the teacher level. As seen in Table 2, no statistically significant difference was found between treatment and control classrooms on any measure, although the effect was consistently in the desired direction.

Measure	RSL		Control		ESP^a^P	ESP^b^P	Variance ComponentsP^c^P		T-ratioP^d^P(df)	p
	Mean(s.e.)	Adj.	Mean(s.e.)	Adj.			Teacher	Residual
		MeanP^e^P		MeanP^e^P
Blending					0.38	0.35
Pretest	1.95 (.30)		1.68 (.30)				0.514	6.013
Posttest	4.36 (.61)	4.24	3.13 (.60)	3.18			3.717	10.538	1.39(25)	0.178
Initial Sound Fluency					0.19	0.21
Pretest	6.25 (.81)		6.57 (.80)				4.753	34.944
Posttest	11.01 (.91)	11.03	9.64 (.91)	9.58			5.487	50.347	1.22(25)	0.235
Letter-Word Identification					0.21	0.19
Pretest	6.88 (.70)		6.42 (.69)				4.473	17.757
Posttest	13.82 (.94)	13.59	12.86 (.92)	12.94			9.134	21.078	.78(25)	0.443
Rhyming					0.20	0.18
Pretest	2.00 (.23)		1.78 (.23)				0.234	4.570
Posttest	5.56 (.67)	5.49	4.91 (.66)	4.92			4.694	10.739	.70(25)	0.493
Passage Comprehension					0.11	0.09
Pretest	4.98 (.21)		4.87 (.21)				0.235	3.048
Posttest	5.90 (.32)	5.89	5.68 (.31)	5.69			0.795	4.671	.46(25)	0.647
PPVT					0.06	0.01
Pretest	39.59 (2.34)		38.42 (2.30)				46.481	226.460
Posttest	57.20 (2.19)	56.73	56.35 (2.15)	56.59			42.483	181.796	.07(25)	0.948
Letter Naming Fluency					0.02	-0.10
Pretest	7.65 (1.79)		5.86 (1.75)				31.679	88.816
Posttest	24.46 (2.28)	23.63	24.26 (2.24)	24.76			48.359	175.601	-.48(25)	0.637
1. Results are based on 2-level HLM analyses, with treatment entered at teacher level.
2. None of the "test of pre-test diffferences" are statistically significant (p < .05).
ESP^a^P: Unadj. post MB_RSL_B- Unadj. Post MB_Control_B/sqrt of pooled within-group variance at student level.
ESP^b^P: MeanB_RSL_B-MeanB_Control_B/sqrt of pooled within-group variance at student level, adjusted for pre test differences.
^Pc^PVariance components for pretests and unadjusted posttest scores.
^Pd^PT value for treatment effect, based on 2-level HLM adjusting for pretest differences.
^Pe^PAdjusted post-test mean based on 2-level HLM adjusting for pretest differences.

Table 2. Two-Level HLM Results

Student Performance on Early Literacy Outcome Measures by Treatment

Effect of Implementation Quality:

A closer investigation of the data suggested that variance in the quality of implementation may have led to this lack of statistically significant main effects. To investigate the effect of program implementation, RSL teachers were grouped according to level of implementation (5 high and 8 low) and were compared with controls using a two-level hierarchical linear model (“HLM”) technique (students nested within classrooms). Pre-test scores and level of implementation were treated as covariates. This model was able to account for variability in student-level pre-test scores and teacher-level implementation ratings. This analysis indicated that students in high-implementing classrooms significantly outperformed their control counterparts on the test of blending ability at post-test (meanB_high_B=6.46; meanB_control_B=3.18, Table 3). Students in high-implementing classrooms also significantly outperformed controls at post-test on the test of initial sound fluency (meanB_high_B= 13.79; meanB_control_B=9.60), and rhyming (meanB_high_B=7.12; meanB_control_B=4.91). There was no significant difference between low-implementing and control classrooms on any of these measures.

Table 3. Two-Level HLM Results:

Comparison of Means by Level of Implementation

Measure	High RSL		Low RSL		Control		ESP^b^P	ESP^c^P	Variance ComponentsP^d^P
	Mean (s.e.)	Adj.	Mean (s.e.)	Adj.	Mean (s.e.)	Adj.	ESP^b^P	ESP^c^P	Teacher	Residual
	Mean (s.e.)	MeanP^a^P	Mean (s.e.)	MeanP^a^P	Mean (s.e.)	MeanP^a^P			Teacher	Residual
Blending							1.09	-0.10
Pretest	2.31 (.49)		1.73 (.38)		1.68 (.30)				0.545	6.000
Posttest	6.76 (.80)	6.46	2.86 (.62)	2.87	3.13 (.48)	3.18			2.006	10.530
Rhyming							0.71	-0.13
Pretest	2.29 (.38)		1.83 (.29)		1.78 (.23)				0.215	4.584
Posttest	7.27 (1.01)	7.12	4.51 (.79)	4.51	4.90 (.61)	4.91			3.810	10.773
Initial Sound Fluency							0.60	-0.03
Pretest	6.17 (1.35)		6.31 (1.05)		6.58 (.82)				5.135	34.936
Posttest	13.69 (1.31)	13.79	9.40 (1.02)	9.40	9.65 (.80)	9.60			3.080	50.604
Letter-Word Identification							0.49	0.01
Pretest	8.42 (1.08)		5.94 (.85)		6.40 (.65)				3.829	17.792
Posttest	15.97 (1.44)	14.60	12.51 (1.13)	12.98	12.85 (.87)	12.94			7.967	21.098
Passage Comprehension							0.35	-0.06
Pretest	5.26 (.34)		4.84 (.26)		4.89 (.21)				0.234	3.049
Posttest	6.52 (.50)	6.41	5.53 (.39)	5.57	5.67 (.30)	5.69			0.707	4.678
Letter Naming Fluency							0.28	-0.33
Pretest	11.41 (2.77)		5.34 (2.17)		5.85 (1.67)				28.242	88.915
Posttest	31.57 (3.26)	27.88	20.08 (2.55)	21.04	24.15 (1.98)	24.70			33.332	175.799
PPVT							0.09	-0.03
Pretest	46.70 (3.42)		35.20 (2.66)		38.40 (2.08)				33.001	226.332
Posttest	62.20 (3.34)	57.55	54.11 (2.61)	56.25	56.32 (2.03)	56.60			35.315	182.229
1. Results are all based on 2-level HLM analysis, with treatment entered at the teacher level.
2. Among the nine "tests of pre-test differences," only that on PPVT was statistically significant, with high
RSL teachers scoring significantly higher than Control teachers (TB₍₂₄₎_B=2.07, p=.049).
3. Tests of group differences on the adjusted post-test means are presented in Table 3 (or Tables 3-11).
^Pa^PAdjusted post-test mean, based on 2-level HLM analysis adjusting for pretest differences.
^Pb^PMeanB_High_RSL_B - MeanB_Control_B/sqrt of pooled within group variance, adjusted for pretest differences.
^Pc^PMeanB_Low_RSL_B - MeanB_Control_B/sqrt of pooled within group variance, adjusted for pretest differences.
^Pd^PVariance components for HLM analyses on pretest and unadjusted post test means.

Discussion

This study indicates that students using the RSL curriculum with a high level of fidelity outperformed students in control classrooms on key early literacy skills. High-implementing classrooms outperformed controls on word blending, initial sound fluency, and rhyming. Results of this 2003-2004 study indicate that the use of the RSL program fosters early literacy skill development, positioning emergent readers for greater academic success in future years. After one academic year of implementation, children in high-implementing classrooms performed significantly better than children in control classrooms on the phonological awareness skills that are considered to be strong predictors of reading success in the early primary grades.

References

Dunn, L. M., & Dunn, L. M. (1997).Peabody picture vocabulary test-III (3P^rd^P ed.), Circle Pines, MN: American Guidance Service, Inc.

Good, R. H., & Kaminski, R. A. (Eds.) (2002). Dynamic indicators of basic early literacy skills (6P^th^P ed.), Eugene, OR: Institute for the Development of Educational Achievement, Available: http://dibels.uoregon.edu

National Institute of Child Health and Human Development (2000).TReport of the National Reading Panel. Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instructionT (NIH Publication No. 00-4769). Washington, DC: U.S. Government Printing Office.

Wagner, R. K., Torgesen, J. K., & Rashotte, C. A. (1999).Comprehensive test of phonological processing, Austin, TX: PRO-ED Publishing, Inc.

Woodcock, R. W., McGrew, K. S., Mather, N., & Schrank, F. A. (2001). Woodcock-Johnson III Tests of Achievement, Itasca, IL: Riverside Publishing.

Anne L. Fetter PhD

Chief Statistician and Research Methodologist

LeapFrog SchoolHouse Research

6401 Hollis Street #150 Emeryville

CA 94608 USA

AFetter@LeapFrogSchoolHouse.com

Learning Objects Recommendation in a Collaborative Information Management System

Introduction

In a distributed learning environment there is likely to be large number of educational resources (web pages, lectures, journal papers, learning objects, and so on) stored in many distributed and differing repositories on the Internet. Without guidance, students will probably have great difficulties in finding the reading material relevant for a particular learning task. The meta-data descriptions concerning learning object representation provide information about properties of the learning objects. However, the meta-data by itself does not provide qualitative information about different objects nor does it provide information for customized views. This problem is becoming particularly important in Web-based education where the variety of learners taking the same course is much greater.

Vice versa, the courses produced using adaptive hypermedia or intelligent tutoring system technologies are able to dynamically select the most relevant learning material from their knowledge bases for each individual student. Nevertheless, generally, these systems can’t directly benefit from existing repositories of learning material (Brusilovsky & Nijhavan, 2002).

This paper provides a contribution to this issue. The basic idea is to appropriately gather different agent-based modules which would help students classify domain specific information found on the Web and saved as bookmarks, in order to recommend these documents to other students with similar interests and to notify periodically new potentially interesting documents. The system is developed to provide immediate portability and visibility from different user locations, enabling the access to personal bookmark repository just by using a web browser. Detailed information about technical characteristics can be found in (Bighini & Carbonaro, in press; Bighini et al., 2003).

Recently, learning objects (LO) have been the center of attention in e-learning mechanisms and designated as atomic units of knowledge (Wetterling & Collis, 2003). In particular, learning object metadata tags facilitate rapid updating, searching and management of content by filtering and selecting only the relevant content for a given purpose. Searchers can use a standard set of retrieval techniques to maximize their chances of finding the resources via a search engine (Recker & Wiley, 2001).

Our collaborative filtering approach supports the automatic recommendation of relevant learning objects.

In this context, standard keyword search is of very limited effectiveness. For example, it cannot filter for the type of information (tutorial, applet or demo, review questions, etc.), the level of the information (aimed at secondary school students, graduate students, etc.), the prerequisites for understanding the information, or the quality of the information.

The starting point is the use of statistical information extraction and natural language parsing techniques to automatically derive classificatory and metadata information from primarily textual data (web pages, Word, postscript or similar documents, etc.). While still challenging for large ontologies, text classification methods which semantically categorize an entire document are now relatively well-understood, and provide a good level of performance.

The next paragraphs describe how to extend the described recommender system to address issues like trying to determine the type or the quality of the information presented in the personalized learning environment.

The paper is organized as follows. We introduce the learning objects recommendation process, obtained considering student and learning material profiles and adopting filtering criteria based on the value of selected metadata fields. Following, we present the conclusion of the paper.

The E-Learning Recommendation

The automatic recommendation of relevant learning objects is obtained considering student and learning material profiles and adopting filtering criteria based on the value of selected metadata fields. Our experiments are based on SCORM compliant LOs. For example, we use the student’s knowledge of domain concept to avoid recommendation of highly technical papers to a beginner student or popular-magazine articles to a senior graduate student. For each student, the system evaluates and updates his skill and technical expertise levels. The pre-processing component developed to analyze the information maintained in LOs is able to produce a vector representation based on term weighting that can be used by described collaborative recommendation system.

To obtain the loading of some didactical source and its classification, we analyze the imsmanifest.xml file to extract .htm and .html files and examine the content. We consider the following metadata to provide the corresponding technical level:

Ø difficulty: represents the complexity of the learning material, ranging from “very easy” to “very difficult”;

Ø interactivity level: represents the interactive format, ranging from “very low” (only static content) to “very high”;

Ø intended end user role: represents the user type (for example student or teacher);

Ø context: represents the instructional level necessary to take up LO.

The difficulty level is explicitly loaded into our database (in most cases, LMSs use this value). Difficulty and the other values are combined to characterize technical level of learning material ranging from 0 to 1 and representing how demanding is the LO. If some of these fields are not present in the manifest file (they are not required), we consider their average value.

Our system also considers the user’s skills to express cleverness as regards different categories. This value ranges from 0 to 1 and it depends initially on the context chosen from the user during his/her registration (primary education, university level, and so on). During the creation of a new category (for example, when a lesson is saved) we consider the user’s skill value equal to the resource technical level, presuming that if a user saves a learning material then he could be able to make use of it. The user’s skill level is updated when a new resource is saved, taking into account its technical level and the user’s skills in that category. Starting value for user’s skills parameter, its update frequency, the increment or decrement value and the difference between technical level and user’s skills necessary to obtain a recommendation outcome from the following experimental tests. They are easily adaptable, though.

Despite SCORM is widely used in learning environments, it presence in the web is very little; furthermore, most of the LOs that are published are not free. So, we have created a SCORM compliant learning material using the abstract of hundred and hundred of papers in .html version from scientific journals published on the web. We have linked an imsmanifest SCORM file to each paper. Then, we have simulated ten users with different initial profiles (based on the field of interest and the skill level) and saved, in four turns, ten learning resources for each user, obtaining 400 LOs.

The precision of recommendation phase, evaluated considering the resource difficulty and the user’s skills, exceeds 80%. It is important to note that the recommendation is made using skill and technical levels (the resource difficulty is one of the parameters, even if the most meaningful); in fact, users rarely know the technical level of a recommended resource; they rather know about its difficulty. Moreover, the categories of recommended learning material correspond to the user’s interests, in almost all of the tests carried out.

Considerations

The paper describes how to extend a collaborative recommender system in e-learning framework, addressing issues like trying to determine the type or the quality of the proposed information. The automatic recommendation of relevant learning objects is obtained considering student and learning material profiles and adopting filtering criteria based on the value of selected metadata fields. Our experiments to test the system's functionality are based on SCORM compliant LOs; we use artificial learners to get a flavour of how the system works.

References

Bighini, C., & Carbonaro, A. (in press). InLinx: Intelligent Agents for Personalized Classification, Sharing and Recommendation. International Journal of Computational Intelligence.

Bighini, C., Carbonaro, A., Casadei, G. (2003). InLinx for Document Classification, Sharing and Recommendation. Paper presented at the International Conference on Advanced Learning Technologies, July 9-11, 2003, Athens, Greece.

Brusilovsky, P., & Nijhavan, H. (2002). A Framework for Adaptive E-Learning Based on Distributed Re-usable Learning Activities. Paper presented at the World Conference on E-Learning, October 15-19, 2002, Montreal, Canada.

Recker, M., & Wiley, D. (2001). A non-authoritative educational metadata ontology for filtering and recommending learning objects. Journal of Interactive Learning Environments, 9 (3), 255-271.

Wetterling, J., & Collis, B. (2003). Sharing and Re-Use of Learning Resources Across a Trans-National Network. Journal of Interactive Media in Education, 1, retrieved October 15, 2004 from

http://www-jime.open.ac.uk/2003/1/reuse-19.html.

Antonella Carbonaro

Department of Computer Science

University of Bologna

Mura Anteo Zamboni 7

I-40127 Bologna, Italy

Tel.: +39 0547 338830

Fax: +39 0547 338990

carbonar@csr.unibo.it

Designing Learning Objects in Electronics Engineering Education

1. Introduction

Internet and related technology have been used engineering education. Many of these institutions also supplement regular classroom teaching with additional Web based material [1-5]. Engineering education have a complex structure. Teaching these concepts with scarce sources to students is difficult for instructors as well [6]. To address problems in teaching of electronics engineering, we have developed a many learning objects have been prepared for teaching fundamental topics in Operational Amplifiers. In this study, we will present the learning objects prepared for electronic engineering education. In addition, others animations that can be found on the Internet will be shown.

2. Designing Learning Objects in Operational Amplifier

Operational Amplifiers (Opamps) are a basic building block in many linear and non linear signal processing operations. Therefore both electrical engineers and electronic teachers must know the basics of them, going from circuits based on opamps modelled ideally to circuits with opamps using more realistic non ideal models. Unfortunately, many electrical engineering undergraduate curricula do not have a full course devoted to teaching opamp circuits. There are many reasons that account for this fact. The number of topics to teach at the undergraduate level has increased considerably during the last several years. Thus, educators have to squeeze or leave out some topics. This is the case with opamps, which are now taught within the linear electronics course.

This application covers the main topics on operational amplifiers (opamps). These main topics cover only some applications of opamps. This is so because there is a great deal of applications available for opamps. Basic Configurations; The three basic opamp configurations, namely, the inverting amplifiers, the noninvert. amplifier, and the difference amplifier (see Fig 1-3).

Macromedia Flash software is a tool for creating Learning objects. Interaction is achieved through the use of Flash’s scripting language. This language makes it possible for LO to detect user input such as mouse or keyboard events and respond with scripted actions. This tool was the primary technology we used to create interactive components [4].

The module on operational amplifier provides a practical solution to learning problem. This module includes six simulations illustrating the different configurations operational amplifier (inverting, noninverting, follower, multiple input, differential, and instrumentation amplifier).

On learning objects, Op-Amp’s input voltage feedback and input resistance can be adjusted with the arrows on it. After all, a test page which consists of questions related to random values assigned and having various connections operational amplifier circuits comes next (see Figure 1-3).

Figure 1. A screen shot of inverting amplifiers

Figure 2. A screen shot of differential amplifier

Figure 3. A screen shot of Instrumentation amplifier

In Figure 4 shown that one of the most used connection types on Instrument is the instrumentation amplifier.

3. Conclusion

These learning objects enable educators to provide a learning environment beyond the bounds of the classroom either to supplement their in-class teaching or as part of a distance learning course. These applications include the inverting amplifier, the noninverting amplifier, the follower, multi input amplifier, differentiator amplifier, and instrumentation amplifier. Thus, what was unreasonable in class is now very reasonable through animation technology.

We believe the learning objects improve the effectiveness of learning is the opportunity for greater student engagement during study. Many of the lessons in the LO present the material with interactive components that are more engaging than traditional textbook reading. The LO modules improve the effectiveness of learning is the informal responses we have received from students using the modules. Nineteen undergraduate students enrolled in Operational Amplifiers Course (Department of Electronic and Computer Science, Mugla University,) were asked for their opinion of the Learning Objects which was accessible from the class web site. The students unanimously agreed that the lessons and animations explaining the operational amplifier and the inverting amplifier model were more helpful than reading the textbook alone.

For more information these Learning Objects Hhttp://www.mu.edu.tr/private/ayhan/lo/Hopamp/

References

[1] Lee, M. P., & Sullivan, W. G. (1996). Developing and Implementing Interactive Multimedia in education. IEEE Transactions on Education, 39 (3), 301-305.

[2] Millard, D. L., & Burnham, G. (2001). Innovative Interactive Media for Electrical Engineering Education. Paper presented at the IEEE Frontiers in Education 2001 Conference, October 10-13, 2001, Reno, Nevada, USA.

[3] Penfield, P. (1996). Education via Advanced Technologies. IEEE Transactions on Education, 39 (3), 436-443.

[4] Barretto, S. F. A., Piazzalunga, R., & Ribeiro, V. (2003). Combining interactivity and improved layout while creating educational software for the Web. Computers & Education, 40 (3), 271-284.

[5] Istanbullu, A., & Güler, I. (2002). Computer assisted learning for biomedical engineering education:Tools. Paper presented at the 23P^rd^P Annual International Conference of the IEEE Engineering in Medicine And Biology Society, October 25-28, 2003, Istanbul, Turkey.

[6] Istanbullu, A., & Güler, I. (2004). Multimedia Based Medical Instrumentation Course in Biomedical Engineering. Journal of Medical Systems, 28 (5), 447-454.

Ayhan İstanbullu

Department of Electronics and Computer Education,

Faculty of Technical Education, Mugla University, Mugla, Turkey

Tiayhan@mu.edu.tr

İnan Güler

Department of Electronics and Computer Education,

Faculty of Technical Education, Gazi University, Ankara, Turkey

iguler@gazi.edu.tr

When schools closed: teacher experiences using ICTs

Introduction

In 2003, a sick physician from mainland China brought to Hong Kong a deadly virus that was to spread fear throughout the territory and the rest of the world. By May 2003, the Severe Acute Respiratory Syndrome or SARS had infected 1755 people in Hong Kong and claimed 304 lives. It spread rapidly via international air travel from China to many cities across the world, resulting in over 8000 cases of infections and 774 deaths.

Schools in Hong Kong were closed suddenly to prevent the spread of SARS. Teachers had to re-think their teaching strategies and provide their students with new and different opportunities to work through curriculum requirements. This paper focuses on how some teachers coped with the schools’ closedown and their use of ICTs to support student learning.

The participants

Sixty teachers were asked in a survey to describe what happened in their schools over the period of school closures and what use, if any, ICT played in the continuation of student learning during this time. Eight teachers were then purposively selected for interviews to gain insights into the participants’ experiences of ICT use during the school closure and to discuss possible longer term impacts SARS has had on their schools.

The participating teachers interviewed held widely differing attitudes towards new technologies and their role and impact. Their views ranged from enthusiastic ‘technophiles’ through cynics to ‘technophobes’ and many stances in between (Bruce, 1997; Fox & Herrmann, 2000). In general, there was some unease and a sense of insecurity caused by a lack of familiarity with technologies and their purpose in education and the impact of change itself, creating doubts and suspicions about new technologies.

Interview outcomes

The sudden closure of the schools during SARS, left most teachers unprepared. ‘Due to timetable commitments on the final day before closedown, form teachers did not see all their students. … some parents had already kept their kids at home, fearing SARS infection at school … .’ said one teacher. Few schools offered clear directives to teachers and few teachers felt they had the necessary skills or experience to successfully switch from everyday face-to-face teaching to alternative environments (distance education strategies, the Internet, phone, ordinary post, etc.). Several teachers noted that they felt ‘stunned’ by the sudden change of events. “We just weren’t prepared for this emergency … and we had not had appropriate training to use the Internet …’ Though all teachers in Hong Kong had attended training courses on using ICTs, most felt unable to use this digital environment as the dominant medium to work with. ‘We’d had some experience of using ICTs in class, but this situation was totally new and threw us into a totally different way of thinking about teaching and learning’.

For some teachers interviewed, new technologies offered new opportunities to ‘warehouse’ content, a strategy not often used prior to SARS. From comments made in the interviews, it was apparent that there was a tendency to try to do too much during this period and to use the new digital technologies to deliver large amounts of additional study materials, resources and information for students.

It is clear from the interviews that handling new ways of communicating between staff and students during SARS was not straightforward. All teachers interviewed, whether they saw the new ICTs as an opportunity or a threat, agreed that the SARS closedown had highlighted the fact that there was much that still needed to be learnt about using the technologies appropriately.

Out of the eight teachers interviewed, most felt that there were advantages in working with the new technologies, though the kinds of opportunities available differed greatly between longer term experienced users of ICTs and novice users, and from subject to subject. The SARS induced close down was certainly an opportunity for those willing to experiment with using ICTs to do so intensively, but the fact that the schools closed down so quickly meant that proper preparation time for ordered use of ICT was limited.

Discussion and conclusions

The SARS crisis provided a catalyst for intensive ICT use. Teachers were inducted faster than they liked or thought possible into ways of using ICTs. This period highlighted both the problems and the potential of different ICTs as channels for teaching and learning. Teachers who used the technology during SARS were forced to think differently about ICTs. Discoveries made during SARS have continued to shape and inform subsequent ICT use. This period of ICT use brought to teachers’ attention that these technologies are not pedagogically neutral nor necessarily passive (Idhe, 1990; Levy, 1997), nor are they necessarily appropriate for substituting one form of delivery for another (Bromley, 1998).

Most of the teachers interviewed wished to build on their experiences with using ICT’s during SARS and identified the provision of a school based, on-the-spot level of technical support as key to their continued motivation to do so in an already packed working schedule. This type of technical support is essential to teachers in terms of setting up and maintaining collaboration via the Internet for example, and making sense of and remembering procedures at a time when they genuinely need to apply them. They need to learn technical strategies for using ICTs to maximize students’ participation and making better use of class time. Clear guidelines need to be set and made explicit and routines established for viewing and responding to emails so that expectations are realistic and can be met without overloading teachers.

In addition students need also to be inducted into the use of ICT’s in order to optimize the potential of their learning experiences in this mode and their efficiency in using it. Consistent with current curriculum directions the use of ICT’s provides an imperative for guiding students towards developing more independent learning strategies in terms of how to set their own goals in relation to those defined by their teachers, how to search for and select information appropriate to achieving those goals, how to work collaboratively using ICT’s with peers and teachers and how to evaluate their own progress and edit outcomes. This impacts also on the current, very exam orientated system of assessing students which compels teachers and students to cover a lot of content, often at the expense of leaning how to learn. In line with current curriculum directions the use of ICTs provides a context for moving away from the examination system towards a more holistic, ongoing form of assessment of work done online or otherwise as well as in portfolios etc. thus putting emphasis on the process as well as the product of school work and in turn freeing teachers and students from the imperative of covering the content of the exam syllabus to focusing on more developmental, student friendly ways of working in the classroom and online. This would allow teachers and students more time for the ‘radical ways of teaching and learning’, described by one teacher in this study, “ that this technology seems to support well”.

The interviews with these teachers revealed conflicting views and beliefs, and varied experiences and concerns about the new technologies in educational settings. There is no single level of knowledge or ability in using new technologies, and there can be no single voice from those teachers interviewed. What did became clear as a result of ICT use by teachers and students during SARS was a more intense and reflective examination of the opportunities the technology offered, and its impact on education in Hong Kong.

References

Bromley, H. (1998). Introduction: data-driven democracy? Social assessment of educational computing. In H. Bromley & M. Apple (Eds.), Education, Technology, Power, Albany, NY: Suny Press, 1-28.

Bruce, B. C. (1997). Critical issues. Literacy technologies: What stance should we take? Journal of Literacy Research, 29 (2), 269-309.

Fox, R., & Herrmann, A. (2000). Changing media, changing times: coping with adopting new educational technologies. In T. Evans & D. Nation (Eds.), Changing University Teaching: Reflections on Creating Educational Technologies, London: Kogan Page, 73-84.

Idhe, D. (1990). Technology and The Lifeworld: Garden to Earth, Bloomington and Indiana: IndianaUniversity Press.

Robert Fox

Associate Professor

Division Information & Technology Studies

Faculty of Education, University of Hong Kong

Pokfulam Road, Hong Kong, SAR

China

bobfox@hku.hk

Tel: +852 2859 8014

Fax: +852 2517 7194

E-CaD: A New Curriculum Model at the University of Phoenix

Introduction

Distance educators are always interested in major instructional developments at the University of Phoenix (UOP). Recently, UOP has launched a new online educational design model know as E-CaD (Enhanced Curriculum & Delivery Model). My discussion will highlight the major features of E-CaD as it relates to the teaching and learning process.

Educational Background

The University of Phoenix (UOP) is recognized as a leader in adult education. The institution was established in 1976 and serves a student population of over 213,000 students who are involved in on-ground and online classes. The consolidated enrollment of its educational programs makes it the largest private higher education organization in the United States. Students can attend classes online and on-ground at 151 campuses that are located in 25 states, Canada and Puerto Rico. The average student age is 34 years old and 56% are females and 44% are males (UOP Fact Book, 2004).

A core of 347 full-time faculty members provides essential leadership by establishing academic standards and supervising curriculum development. Approximately 17,000 adjunct teachers (4, 000 online) are actively engaged in teaching a diversity of undergraduate and graduate degree programs (UOP Fact Book, 2004). The University is considered an innovative institution that has increased access to higher education while reducing the costs of developing online degree programs by:

Ø using a centralized course development process to ensure quality control and reduce development costs;

Ø effectively using educational technology to deliver the same curriculum to more students;

Ø providing flexible access to classes (Twigg, 2001).

The University of Phoenix has been criticized for using a curriculum based on uniformly prepared instructional materials. Educators are concerned that the curriculum restricts teacher creativity and lacks intellectual rigor (Breen, 2004; Farrell, 2003). University leaders respond to this issue by stressing that the curriculum is carefully developed for their degree programs. UOP utilizes a team of faculty members, curriculum managers and instructional designers who develop courses based on the latest theory and practice. Teachers can integrate their subject expertise into their courses through lectures, handouts and online discussion comments which help to personalize the learning environment.

Rationale For Implementing E-CaD

The University of Phoenix strives to be innovative in their online design and delivery of online education. UOP educators and administrators have been studying how to make their delivery model more efficient without sacrificing academic rigor. A real issue became one of scalability because the institution had to find a better way to accommodate more students. Additionally, it is a for profit organization which has to be sensitive to stockholder concerns about the potential for future growth in their online degree programs.

UOP has frequently promoted their small class sizes in their literature. Swenson (2001) states “the low student/faculty ratio and class sizes that average 13 students facilitate active learning and collaboration, encourage time-on-task, and foster high student-faculty interaction” (p. 5). University officials relate that the majority of today’s major distance education schools often have at least 20 or more students per class. Therefore, the curriculum changes represent a major response to market factors which have help to prompt these changes. E-CaD is an instructional format that has been created to enable instructors to facilitate a class size of 20 students (E-CaD, 2004).

The University has been testing various online delivery systems and E-CaD represents the culmination of their research and pilot studies. It is a creative design that has retains an emphasis on essential student skills and subject knowledge but enables instructors to handle larger classes. E-CaD has the following key features:

Student Academic Expectations

Ø students actively participate with substantive remarks in online discussions 4/7 days a week (previously 5/7 days)

Ø final week of class has optional student participation in online discussions (previously students participated all weeks of course)

Ø weekly summaries are optional ( previously these were required)

Faculty Academic Expectations

Ø provide detailed syllabus (change only in specific E-CaD details)

Ø share two weekly online discussion questions (previously 3-6 questions)

Ø freedom to assign weekly online discussion questions to learning teams (previously dialog questions created only for individual students)

Ø share weekly lectures can be optional if course has weekly overview of material in rEsource

Ø respond to student comments 5/7 days in online discussions (no change)

Ø share weekly grade reports with students (no change) (E-CaD, 2004).

The E-CaD model is currently being phased into the various online classes which will require careful modification of the curriculum to fit this new format. UOP facilitators are naturally a little anxious about increased class sizes and the impact that it will have on their work load. The author has taught two online classes (US history & film studies) under the new model and has found that students are actually sharing at least five days a week. Also, grading papers and responding to students online has been quite manageable and no more time consuming. Instructors must be careful to sustain good online presence with more students in their classes. The key is to daily share relevant messages in the main newsgroups and relate to all of the students during each week of class.

Conclusion

E-CaD represents important changes in the delivery of online courses at the University of Phoenix. It affirms a distance education trend of having larger classes to meet the growing demand for online degree programs.

References

Breen, B. (2003). The hard life and restless mind of America’s education billionaire. Fast Company, Available: http://www.fastcompany.com/magazine/68/sperling.html.

Farrell, E. F. (2003). Phoenix’s unusual way of crafting courses. The Chronicle of Higher Education, 49 (23), A10.

UOP Fact Book (2004).University of Phoenix, Phoenix, Az, USA.

E-CaD (2004) University of Phoenix faculty materials, Phoenix, Az, USA.

Swenson, C. (2001). New models for higher education: Creating an adult-centered institution. Globalization: What issues are at stake for universities? Available:

http://www.ulaval.ca/BI/Globalisation-Universities/pages/actes/Craig-Swenson.pdf.

Brent Muirhead D.Min., Ph.D.

Lead Faculty, Area Chair

GBAM Business Communications (Atlanta, Georgia)

University of Phoenix, USA

bmuirhead@email.uoiphx.edu

Teamspace: An Innovative Workspace for Collaborative Academic Computing

Abstract

As personal computer ownership among university students has increased in recent years, university administration are now faced with the challenge of providing value-added computing facilities for their constituents beyond basic access.

Enter the group workspace aiming to promote teamwork and collaboration across machines, location, and time. Unfortunately, many such group workspaces developed thus far have been impractical either due to cost or difficulty of use. Teamspace is a prototype for a public interactive learning workspace designed to be easy for novice users to understand and learn. It addresses the challenges of economics, installation, recovery, robustness and reliability in a way that simplifies and empowers the group work user experience.

Introduction

In many environments, such as universities, widespread individual laptop ownership has fundamentally changed how and when individuals use computers, thereby transforming the notion of a public computer space from that offering computer access to one that provides group workspaces involving a number of diverse devices, operating platforms, and media. The challenge, then, is to provide infrastructure for collaboration among team members using data and computing resources on separate computers working together on a single task.

Building further on our previous Interactive Room Operating System (iROS) [2,5], the StanfordUniversity iWork Research Group has developed Teamspace, an innovative workspace for collaborative learning. Teamspace facilitates collaboration through one or more large displays visible to all group members, representing the notion of a “public desktop,” together with two iROS components, PointRight [3] to control the public desktop from any connected laptop computer, and Multibrowse [4] for filesharing, all in a relatively simple software program that is easy to both install and use.

Modes of Collaboration

Traditional efforts at collaboration using traditional paradigms generally result in one of two outcomes: 1) group members crowd around a single computer where only some can view the screen and only one can control input devices, or 2) group members divide the workload, then proceed to work in isolation on their own computers, sending results to a single person designated to compile the disparate results. In contrast, an effective system provides both shared and private spaces. IROS achieves this with the following:

(a) Event Heap. General framework for managing events.

(b) PointRight. Pointer redirection that enables a pointing device on any machine to serve as a pointer on any other. In the context of Teamspace, PointRight is used to control the public desktop from any connected laptop computer.

(c) MultiBrowse. Multibrowse enables any file or URL from one machine to be actively “pushed” onto the display of another.

Transforming the User Experience

The original iROS framework require extensive and expensive installation and maintenance by highly technical staff, which can be a prohibitive overhead for widespread adoption. Teamspace builds upon iROS, extracting key components in a simple, easily configurable, cross-platform end-user application requiring little or no technical administration or maintenance.

Installation and configuration

The user experience of installation is designed to be straightforward and simple.

Figure 1. Dragging and dropping file or URL icons into the Teamspace window will cause them to be multibrowsed to the corresponding destination machine, which may be one of laptops connected to the space or the public desktop itself

User Studies

In order to learn how university student users utilized and responded to Teamspace, we conducted user studies on fifty undergraduate students representing a wide range of technical and non-technical majors, all with no prior experience using iROS:

i) “Random.” Teams of three to four paid volunteers were arbitrarily formed and given a specific group task

ii) “Existing.” Previously formed student teams working on real, existing group projects for courses, student clubs, etc.

Random Groups

We were surprised at the quantity of time participants spent offline getting to know one another and formulating ideas verbally. Users tended to work relatively independently, only collaborating at the beginning (to divide the work) and end (to compile the work) of the study session. During these moments of collaboration, they tended to multibrowse to the public desktop (and not each other) only.

Existing Groups

Participants from existing groups, sharing a common external purpose and already familiar with one another, were much more apt to collaborate, not only on the project at hand but also in learning the Teamspace technology. These users tended to multibrowse throughout the session both to the public screen and to one another’s laptops. One unexpected result was that multiple sends from different sources sometimes resulted in loss of information or communication.

These participants felt considerably more comfortable vying for the group screen pointer, resulting in increased collaboration on the group display and a more frequent switching between private and public workspaces.

Figure 2. Stanford students using Teamspace

Conclusion

Based on these preliminary studies and public student use at Stanford, we believe Teamspace has great potential of serving as a viable next-generation application for academic computing facilities. In particular, we believe Teamspace’s zero-administration operation, low cost, ease of use, and compelling interaction modes make it ideal for collaboration especially in academic learning environments.

Acknowledgements

Our thanks to David Futey and Richard Holeton for their support of this project, and for equipment and funding from Stanford Academic Computing, the Stanford School of Engineering, and the Wallenberg Global Learning Network.

References

Ionescu, A., Stone, M., & Winograd, T. (2002). WorkspaceNavigator: Capture, Recall, and Reuse using Spatial Cues in an Interactive Workspace. Stanford Computer Science Technical Report 2002-04.

Johanson, B., & Fox, A. (2002). The Event Heap: A Coordination Infrastructure for Interactive Workspaces. Paper presented at the 4P^th^P IEEE Workshop on Mobile Computer Systems and Applications (WMCSA-2002), June 20-21, 2002, Callicoon, NY, USA.

Johanson, B., Hutchins, G., Stone, M., & Winograd, T. (2002). PointRight: Experience with Flexible Input Redirection in Interactive Workspaces. Paper presented at the 15th Annual ACM Symposium on User Interface Software and Technology, October 27-30, 2002, Paris, France.

Clara C. Shih, Armando Fox and Terry Winograd

Computer Science Department, StanfordUniversity

StanfordCA, USA

Tel: +1 847 722-9542

cshih@cs.stanford.edu

fox@cs.stanford.edu

winograd@cs.stanford.edu

Field-Report of the Java Intelligent Tutoring System

Abstract

The Java Intelligent Tutoring System (JITS) was designed and developed to support the growing popularity of the Java programming language and to promote web-based personalized education. While most ITS require the teacher to author problems with corresponding solutions and navigate the student towards specific pre-determined solutions; the Java Intelligent Tutoring System fully supports individual student development by “intelligently” guiding the student as s/he pursues a solution. JITS is designed with a revolutionary user-modeling system and an embedded logic module called JECA (Java Error Correction Algorithm) [1]. JECA determines the “intent” of the student’s code submission by rigorous scanner-parser analysis [2]. JITS was designed for entry level College and University students and is currently being field-tested at the Sheridan Institute of Technology and Advanced Learning by students in the School of Applied Computing and Engineering Sciences, Ontario, Canada. This paper presents the findings of the field-report including qualitative and quantitative investigation results.

Introduction

JITS is a fully implemented web-based multi-user multi-threaded distributed ITS. Figure 1 depicts the significant components of the infrastructure. JITS models the user and tracks all interactions between the ITS and the student to an ORACLE database [3]. This information is examined by JITS to develop a precise cognitive model for each student in the system [4]. This in turn provides information that JITS can use to effectively tutor the student.

Figure1. JITS multi-threaded distributed web-based infrastructure

The user interface is based on a presentation format implemented in many popular Integrated Development Environments used by professional programmers (e.g., Visual Café, JDeveloper, etc.). The JITS user interface is presented in Figure 2.

The core module for analysis is the Java Error Correction Algorithm (JECA). This unit provides the necessary information the ITS to model the user and to effectively tutor the student. The goals JECA are to:

i) intelligently recognize the ‘intent’ of the student;

ii) analyze the student’s code submission;

iii) ‘auto-correct’ where appropriate (e.g., converting “While” into the keyword “while”, “forr” into “for”, etc.);

iv) learn individual students’ misconceptions, and categorize the types of errors s/he makes;

v) produce a ‘modified code’ that will compile (or bring the code closer to a state of successful compilation);

vi) produce a ‘modified code’ that will meet the program specifications (or bring the code closer to meeting program specifications); and

vii) prompt the student programmer for information when necessary via well-defined hint support structures.

Figure 2. JITS User Interface

JITS intelligently examines the student’s submitted code and determines appropriate information that needs to be used by the ITS feedback mechanism based on a number of factors such as the student’s skill, the cognitive model of the student, collective student model and problem details.

Methodology

The methodology employed in this research project is supported by two distinct research components. The first component is related to the manner in which JITS was designed and constructed. In this research section, students and professors using the prototype JITS offered suggestions and comments for the improvement of JITS. The new knowledge was fed back into the re-design and re-construction of JITS. Beyond the initial development of JITS a cyclic process was used: design, develop, test, modify, re-design, re-develop, etc. This research methodology involved qualitative instrumentation including observation, surveys and personal interviews.

The second component of the methodology is related to the manner in which JITS was evaluated. In order to determine the degree and quality of learning that took place by students using the Java Intelligent Tutoring System, a quantitative investigation on performance scores was conducted. The research methodology for this section involved performing a pre-test for the Control Group (i.e., C) and the experimental group (i.e., JITCS) at the beginning of the term and a post-test to both groups at the end of the term. Thus, the methodology was an experimental design with repeated measures. Participants in the JITS class also served as their own controls. As a result, the researchers were able to compare pre-test and post-test performance differences as well group differences (i.e., C versus JITSC).

Participants

The population of this study were students in their first year of College taking a beginner Java programming course at SheridanCollege. From May to September 2004, there were two classes of this course. The experimental group was located at the Davis campus (i.e., JITCS). The control group was located at the Trafalgar Road campus (i.e., C). Fourteen students consented to try the Java Intelligent Tutoring System. Every week ½ to 1 hour long sessions were conducted to elicit specific information about their experience while using JITS. JITSC students had continuous access to JITS as it was available 24/7. Additionally, four professors were selected for this study to offer their opinions on JITS.

Conclusions

Table 1 presents the descriptive statistics for C and JITSC. The students who used JITS outperformed the students in the traditional classroom. A two-way ANOVA with repeated measures was conducted confirming these results, F(1,35) = 4.162, p = .049, indicating there was a significant statistical difference in performance scores between the two groups. Table 2 presents these statistics. Figure 3 represents a visual plot of the differences between C and JITSC. Both researchers are very excited about the success of JITS to increase student performance in this field-test. Ongoing field-tests are being conducted and others are scheduled for January 2005.

Table 1. Descriptive Statistics for C and JITSC

	Group	Mean	Std. Deviation	N
Pre_Test	C	61.91%	12.52	23
	JITSC	64.93%	15.17	14
Post_Test	C	66.25%	16.79	23
	JITCS	80.73%	6.75	14

Table 2. Two-way ANOVA with Repeated Measures: Between-Subjects Effects for C and JITSC

Source	Type IV Sum of Squares	df	Mean Square	F	Sig.
Intercept	326268.963	1	326268.963	1020.396	.000
Group	1330.710	1	1330.710	4.162	.049
Error	11191.161	35	319.747

Figure 3. Performance of C and JITSC students using mean grades as data

References

[1] Sykes, E. R., & Franek, F. (2004). Presenting JECA: A Java Error Correcting Algorithm for the Java Intelligent Tutoring System. Paper presented at the IASTED International Conference on Advances in Computer Science and Technology, November 22-24, 2004, St. Thomas, Virgin Islands, USA.

[2] Aho, V. A., & Peterson, G. T. (1972). A Minimum Distance Error-Correction Parser for Context-free Languages. SIAM Journal of Computing, 1, 305-312.

[3] Sykes, E. R., & Franek, F. (2004). A Prototype for an Intelligent Tutoring System for Students Learning to Program in Java. International Journal of Computers and Applications, 1, 35-44.

[4] Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive Tutors: Lessons learned. The Journal of the Learning Sciences, 4, 167-207.

Edward R. Sykes

School of Applied Computing and Engineering Sciences

Sheridan College, Ontario, Canada, L6H 2L1

ed.sykes@sheridanc.on.ca

Franya Franek

Department of Computing and Software,

Faculty of Science,

McMasterUniversity,

1280 Main Street W., Hamilton,

Ont., Canada, L8S 4L8

franek@mcmaster.ca

Use of synchronous Virtual Class in industrial environments to contrast Web-Based Training (WBT) drop out rate

eLearning (intended in particular as WBT) has, since its appearance, been regarded as the “magic” solution for employees training in a fast changing and challenging Global Economy environment.

In fact eLearning can respond very well to the following strategic needs:

Ø Reach vast employee population

Ø Allow Just In Time and Just Enough training experiences

Ø Maximize efficiency in the training delivery (logistics and time)

Ø Standardization of content

Ø Consistent tracking and reporting of training results

However, after the first wave of enthusiasm, eLearning initiatives have often suffered high drop out rates compared to the traditional classroom training.

The problem, for companies willing to utilize eLearning, is therefore to make sure that the investment made to deploy eLearning is utilized effectively.

In our experience we found two main issues causing high drop rate for eLearning within an industrial/enterprise environment: Time and Motivation.

Time

Time is nowadays a critical asset for both employees and company; the increased working efficiency necessary to fight the challenges of the Global Economy, doesn’t leave much time free for training; it is also very difficult, in general, to find suitable time to access eLearning material during the work time (urgent activities or disturbances by colleagues and telephone calls easily disrupt people’s focus from training).

Does this mean that eLearning will never be effective in a working environment?

I believe that, in order to overcome the Time issue, Learning must firstly become a recognized and visible value across the full “chain of command” of the Enterprise. Time for training can only be found when Top Management recognizes in public that employee development is a strategic activity for the Company and when Middle Management promotes the value of the training by setting objectives and goals for the people which also include the development of personal skills.

Motivation

Motivation for training can be mainly defined as two types:

Ø Self motivation, when a person is willing to develop himself further for attaining job or personal related objectives

Ø Environmental motivation, meaning that the environment where one lives or works requires the acquisition of new skills or update of present ones

eLearning and WBT in particular, can work well with people bearing the first type of motivation as it requires a lot of self responsibility and a very good management of one’s own time. The person who is self motivated will be willing to use even available free time to access learning material and will be focused to reach the learning objectives.

The second type of motivation is, unfortunately, of wider occurrence when dealing with employees training; examples of needs could be new processes to be applied in the Company, Project Management, People management, deployment of new software tools and so on.

In this case we have to design the eLearning experience very carefully in order to reach the desired effectiveness.

How can eLearning, with adequate support, become more effective

We have noted that people have a natural preference for traditional classroom training mainly for the following reasons:

Ø Time off the job: The training experience is rewarding as it is detached from the daily routine; people like to join a class, and concentrate on learning in a comfortable environment

Ø Social environment (Classmates and Instructor) boost motivation to learn and share experiences: people learn that other people might face similar problems and can be guided and motivated in learning by skillful instructors

These considerations have led us thinking that if we could in some ways “Socialize” the eLearning experience we could attain an increase of motivation for the students to reach completion of their training programs.

We have therefore focused on 2 main subjects:

Ø The concept of Class as a social environment (the group)

Ø The need for an effective communication tool that takes into account natural interactivity among people (social interaction)

The use of a synchronous Virtual Class has proven to be a very good solution; we have chosen specifically a tool that supports Voice over Internet in order to reproduce the kind of natural interaction that develops among the students and with the teacher in a traditional classroom environment.

The sense of belonging to a group can be obtained by gathering all the people together, at the beginning of the course, in a session where they can get in touch with the instructor and eventually with personal tutors; they can also introduce themselves and interact with classmates.

WBT can be used more effectively after the group is formed and when reference figures or Subject Matter Experts are associated to them. (Experts from inside the Company are very welcomed as they can continue to be a reference point when the course is over.)

Instructors and “Experts” can then organize Virtual Class sessions where they re-group the class and discuss or reinforce specific subjects of the Course.

The Virtual Class also constitutes an ideal tool for tutors to setup personalized sessions with students and motivate the ones who are loosing momentum.

We have employed this technique for a structured program composed of four courses; the results have been very promising and attendance to Virtual Class sessions has been comparable with attendance to traditional classrooms.

Figure1. Attendance comparison between Traditional and Virtual Class

Conclusions

Social interaction can play an important role in decreasing the drop out rate that is often registered when assigning eLearning access (pure WBT) to employees.

Synchronous Virtual Classes can be used to simulate the interactivity among students and with instructors comparable to the interactivity available in traditional classrooms, it can also be used to create and reinforce the role of the group.

A Virtual Class can keep the flexibility of an online environment and, at the same time, increase social contact; a good design of the training experience including Tutoring and internal (thus reachable) Subject Matter Experts also plays a vital role for the success of the initiative. Support by Top and Middle management is a key factor for increasing the motivation and self-responsibility of employees when using an eLearning approach in training.

References

Benbunan-Fich, R. (1997). Effects of Computer Mediated Communication Systems on Learning Performance and Satisfaction: a Comparison of Groups and Individuals Solving Ethical Case Scenarios, Ph.D. Dissertation, Rutgers University/NJIT joint program, USA.

Fata, A. (2003). il tutor online. Psico-Pratika, 5, retrieved October 15, 2004 from

http://www.humantrainer.com/articoli/fata_tutor_formazione_a_distanza.html.

Finch, J., & Montambeau, E. (2000). Beyond Bells and Whistles: Affecting Student Learning Through Technology, retrieved October 15, 2004 from http://www.cofc.edu/bellsandwhistles/index.html.

Griffin, V. (1988), The Craft of Teaching Adults. In Barer-Stein, T. & Draper, J. A. (Eds.), Holistic Learning/Teaching in Adult Education: Would you play a One-String Guitar? Toronto: Culture Concepts Inc., 105-131.

Hiltz, S. R. (1997). Supporting Collaborative Learning in Asynchronous Learning Networks. Invited Keynote at the UNESCO/Open University Symposium on Virtual Learning Environments and the role of the Teacher, April 28, 1997, Milton Keynes, United Kingdom.

IDC (2000). eLearning in Practice: Blended Solutions in Action, White paper available at

http://whitepapers.zdnet.co.uk/0,39025945,60013383p,00.htm.

Kaye, T., & Rumble, G. (1991). Open Universities: A Comparative Approach. Prospects, 21 (2), 214-226.

Meister, J. (2002). Pillars of e-learning success, New York: Corporate University Xchange.

Myers, J. (1991). Cooperative learning in heterogeneous classes. Cooperative Learning, 11 (4).

Giorgio Agosti

ABB Process, Solutions & Services SpA

via Lama 33, 20099 Sesto S. Giovanni (MI), Italy

giorgio.agosti@it.abb.com

Analyzing Empirical Evaluation of Advanced Learning Environments: Complex Systems and Confounding Factors

Researchers of learning technologies have dedicated substantial endeavors to the design and implementation of advanced learning environment (ALE) which are based on various technologies at the front edge, such as multimedia and mobile communication technologies. These novel designs or implementations are given profound expectations to be influential upon learners’ learning outcomes by practitioners of this field. Empirical evaluations are therefore necessary steps for appraising these systems in terms of learning outcomes.

Nevertheless, just as previous studies in the field of intelligent tutoring systems (ITS) have revealed, to evaluate the educational impact of ITS, a representative example of ALE, is a costly affair [4]. One obvious issue to be confronted with would be the inherent ambiguity and uncertainty of human learning. Besides, if the evaluation is undertaken as the form of an experiment or a quasi-experiment, validity and reliability of the quantitative evaluation are essential issues that should be soundly addressed [3]. Fortunately, ALE evaluation is neither the single nor the first case to encounter these concerns. Several long-developed research areas, including educational measurement and experimental psychology, have accumulated plenty of knowledge during past decades dealing with most of these issues.

Even so, it is still not yet the happy ending of the story. A reliable and valid evaluation of ALE remains challenging. The cause is not only due to the classic issues mentioned above, but ALEs are themselves complex artifacts in which a number of sub-components interact in a nonsimple means. It is likely that more confounding factors would be introduced into the evaluation. This scenario is a threat to classically ideal evaluation design that all variables are well controlled, except the treatment. The complexity, of the evaluation task, the to-be-evaluated ALE itself and the interaction between these two systems would be the key issue worth to be analyzed in order to dispel the fog.

We take Simon’s insightful anatomy of complex systems as the basis for analyzing this issue [7]. The hierarchical model proposed by Simon to explain the complexity prevailing among natural phenomena and human-designed artifacts, though may be aged and imperfect 0, can nicely model the complexity embedded in the affair of ALE evaluation. In the rest of this article, this point of view will be further described by employing the evaluation task of adaptive educational hypermedia systems as an example for analysis.

Two Levels of Complexity: the Evaluation-wide System and the Intra-Artifact System

In the task of ALE evaluation, we identify two distinct complex systems: the evaluation-wide system and the intra-artifact system.

On the one hand, the evaluation-wide system is the abstraction of the evaluation task in accompany with various variables. These variables include the ALE to be evaluated, human participants using the ALE, and the experimental instruments such as questionnaires or psychometric tools employed to assess learners’ responses. On the other hand, the intra-artifact system implicates the to-be-evaluated ALE itself such as an intelligent tutoring system for algebra, a learning management system with course sequencing rules and a museum guide system build upon handheld computing devices.

Notice that these two levels of complexity are not irrelevant to each other. There could be interactions and latent linkages between these two worlds. For example, participants of the evaluation, although are part of the evaluation-wide system, participants’ usage and response are likely to be captured and modeled by some ALEs as user models to actuate the artifact’s particular function. Such kind of linkage exactly reflects the complex nature of ALE evaluation.

Figure 1.The hierarchy of underlying factors for AEHS

An Example of Analysis: Evaluating Adaptive Educational Hypermedia Systems

Adaptive Education Hypermedia Systems (AEHS) are user modeling-based systems that can offer learners fit-to-needs learning experience. To achieve this feature, AEHS is constituted by several sub-components which have their own distinct behaviors and properties. Figure 1 conceptually depicts the hierarchical model of this scenario. The AEHS as the intra-artifact system shown in Figure 1 can be decomposed into sub-components by considering the influence of sub-components on the performance of ones at the higher level.

On the other hand, by moving the focus to the part of evaluation-wide system, the hierarchy of the evaluation task is shown in the upper part of Figure 1. Since the evaluation affair can be treat as a kind of measurement, there is no so-called errorless evaluation [6]. By taking the view of classical test theory, the observed scores of learners’ learning outcomes could be decomposed as:

where SB_observed_B is the observed score derived from the measurement, SB_true_B is the unknown true score and E is the measurement error. It can be observed that the construct of interest to be evaluated, learning effect, locates at the bottom of the evaluation-wide system, therefore, is not observable directly. Thus an adequate experimental design (e.g., between-groups random assignment experiment) is essential to control the influence of non-focused factors of the measurement, such as participants’ prior capability and other possible covariates. It is worth noting that in Figure 1, the dashed arrows are edges representing uncertain influence. In this hierarchical model, uncontrolled factors are identified and linked to other sub-components in the system with dashed arrows.

The unique obstacle that ALE evaluation meets is the interaction and linkage between these two complex systems. Sub-components inside the intra-artifact system can also play the role of uncontrolled factors in the evaluation-wide system if the experiment is not well designed. For example, it may be possible that the user modeling modules cannot effectively capture user’s information, and the AEHS may function in the way of non-adaptive manner which is less different to typical hypermedia systems. For this situation, the validity of an experimental design of with- or without- user modeling for comparative evaluation would be questionable.

Conclusions

In this article, we propose to employ hierarchical models and the conception of complex systems to realize confounding factors and their relations inside the evaluation of advanced learning environments. The complexity of the measurement and the artifact introduce more uncertainties into the evaluation affair. The approach of layered evaluation shown in [5] would be a noteworthy research direction regarding the analysis we have shown.

The analysis framework shown by this article is believed to be improvable. The theme this article disclosed, ALE evaluation as a complex system, is worth to be further investigated. The dynamics inside the complex systems 0, the concern of modern test theory [2] and the uniqueness of advance learning environments are topics to be studied as future works.

References

[1] Agre, P. E. (2003). Hierarchy and History in Simon’s ‘Architecture of Complexity’. The Journal of the Learning Sciences, 12 (3), 413-426.

[2] Borsboom, D., & Mellenbergh, G. J. (2002). True Scores, Latent Variables and Constructs: A Comment on Schmidt and Hunter. Intelligence, 30, 505-514.

[3] Chin, D. N. (2001). Empirical Evaluation of User Models and User-Adapted Systems. User Modeling and User-Adapted Interaction, 11, 181-194.

[4] Iqbal, A., Oppermann, R., Patel, A., & Kinshuk (1999). A Classification of Evaluation Methods for Intelligent Tutoring Systems. In U. Arend, E. Eberleh & K. Pitschke (Eds.) Software Ergonomie '99 - Design von Informationswelten, Leipzig, Germany: B. G. Teubner Stuttgart, 169-181.

[5] Karagiannidis, C., Sampson, D., & Brusilovsky, P. (2001). Layered Evaluation of Adaptive and Personalized Educational Applications and Services. Paper presented at the AIED 2001 Workshop on Assessment Methods in Web-based Learning Environments & Adaptive Hypermedia, 21-29.

[6] Schmidt, F. L., & Hunter, J. E. (1999). Theory Testing and Measurement Error. Intelligence, 27 (3), 183-198.

[7] Simon, H. A. (1996). The Sciences of the Artificial (3P^rd^P Ed.), Cambridge, MA: MIT Press.

Hao-Chuan Wang

Institute of Information Science

Academia Sinica

Taiwan

haochuan@iis.sinica.edu.tw

Tsai-Yen Li

Computer Science Department

National Chengchi University, Taiwan

li@nccu.edu.tw

Chun-Yen Chang

Department of Earth Sciences

NationalTaiwanNormalUniversity

Taiwan

changcy@cc.ntnu.edu.tw

The Possibilities for Digital tools for Writing

Most children find learning to write to be a difficult task; in order to construct a piece of writing, the child has to decide on a storyline, select appropriate vocabulary and grammar, arrange the words with respect to punctuation, decide how to spell words (sometimes making substitutions of words at this point when a spelling is perceived to be too difficult), and finally, he has to physically commit the words to paper.

When children learn to write, they traditionally use a pencil and paper for this physical process and, as they gain competence, they are then encouraged to enter their work into word processing software so it can be edited. Most children eventually become able to construct writing directly at the keyboard as well as on paper. It can be argued that keyboard competence is necessary for mature writers, however, contrary to the view of many technology enthusiasts, the keyboard is not be a particularly good tool for young writers who are still learning all the processes. There are problems with the layout of the keys, possible ergonomic problems that have not been fully researched and the presentation of the writing (in a computer font) may have an affect on the child’s understanding of plot and form. One advantage of writing with a pen rather than typing at a keyboard is that for children who are learning to write, the movement involved in forming letters with a pen supports the development of good spelling

Recent technologies including Tablet PCs, Personal Digital Assistants (PDAs) and Digital Pens all offer interesting alternatives to traditional pen and paper; preserving the use of the pen for input whilst allowing for some digital manipulation of the writing once the story has been written. These technologies have not been especially well researched with respect to their usability in classroom environments (surprisingly, neither has the QWERTY keyboard!) and so we have carried out a small investigation of their relative usability with children aged seven and eight in a UK Primary School.

The Tablet PC

This was set up to work just like pen and paper, that is, the child was able to write using the stylus (pen) on the screen (paper) that was lined and scrolled down if there was too much writing to fit on the page. Children who used this method seemed to have no problems in using the device to write although watching them, they adopted some strange writing positions as they attempted to see the screen and reach the tablet at the same time. They were able to use the device with very little instruction and only needed help to save their work once they had finished.

The Personal Digital Assistant

We looked at two PDA applications, a cut down version of Microsoft Word® which uses a transcriber tool to change each handwritten letter into ASCII text, and a note-taking tool that saves the handwriting as an image. The children found using the application that ran Microsoft Word® very difficult to use as they were unfamiliar with the transcriber tool and its’ functions. They had problems in editing the text they had written as it was unclear how to delete any mistakes that were made. The handwriting recognition struggled with the children’s handwriting and quite frequently got the letters they entered wrong. In addition, because the children wrote quite slowly, the software kept putting extra spaces in their words. While this was amusing at first to the children, it soon began to irritate them, again especially because they had such problems repairing the mistakes.

The note-taking tool was the preferred choice of software on the PDA because the children could simply write. However, this application was not without problems; with such a small screen being used, the children found that they had to mostly write one word to a line, which did not help them when trying to write a story. This was not helped by the need to do a lot of scrolling to see what they had previously written.

Using either application, the children struggled to write any kind of stories due to the difficulties they had with the device. One major problem with the PDA was that as the children had such small hands, it was hard for them to write without leaning on the PDA and as the PDA is a touch screen device, whenever the screen is touched unintentionally, additional marks appear on it; the children found this confusing and irritating.

The Digital Pen

The children wrote their stories with digital pens in exactly the same way as if they were using normal pen and paper. This meant that the story quality was unchanged and the children had a paper copy of the story they had written. Using the digital paper, at the end of every page, the user had to tick two boxes to send the writing to a computer and to say that they had finished the page. The children had no problem with this. Once a child had finished their work, the pen was attached to a PC and their story was downloaded from the pen giving them an exact digital replica of what they had written on the paper.

Implications for Learning and Teaching

The PDA was generally unsuitable for the task; there may be other applications for which it can be used by children use but story writing is not one of them! The tablet PC and the digital pen were both very easy to use, the text was highly visible and the potential for later recognition of the writing, and the possibilities for investigation of the writing processes, make them both attractive. We are currently carrying out a large study into the extended usability of these two devices in the primary writing classroom.

Janet C Read and Matthew Horton

Child Computer Interaction Group

University of Central Lancs

Preston, UK

jcread@uclan.ac.uk

Tablet PC: an Enabling device for Teaching and Learning Process

Abstract

The paper describes the use of Tablet PC in “Strength of Material” course, where a Tablet PC was used as a replacement of a blackboard to deliver the live lectures. The lecture material was created during the lecture session by writing on the tablet of the PC that was connected to a video projection system to display the lecture content to the students attending the class. The lecture files were saved and later on placed on a web server for student to have a complete access to the lecture notes through the course web site..

Introduction

The Engineering reading and lecture material consists of text, pictures, sketches, mathematical symbols and equations. It is difficult to create engineering content in digital form using a PC through a mouse and keyboard interface during a live presentation. This problem can be solved through the use of Tablet PCs [1]. It offers a more natural user interface.

The Tablet PC Essentials

The Tablet PCs are available in various form factors: as standalone tablet or as a combination of laptop PC and Tablet. The second form factor allows smoother transition from the use of a laptop to a pure tablet. In the tablet form the screen of the laptop PC acts as a writing pad where user can use their natural writing and drawing abilities with the help of a pen that comes with these devices. They also allow switching the screen display mode from portrait to landscape depending upon the user’s need.

The MS Journal program that comes with the Tablet PCs provides all the necessary tools for controlling the pen movements and the resulting drawings. The line width and color can be adjusted through the program menus. It also provides erasure as well page numbering functions. The author can flip through the pages by simply pressing on the arrows located at the bottom right corner. The intuitive program interface emulates a writing pad and it is very easy to use. The hand written text can be converted to regular typed text through a hand writing recognition program that is part of it. It is very easy to master the use of this program.

Use of Tablet PC in Teaching and learning Process

In the traditional lecture delivery mode the instructors usually use the chalk and blackboard or they prepare PowerPoint slides. It is a very time consuming process to prepare the learning modules that are highly math and graphics oriented. Therefore, the use of digital content within engineering teaching practice has not penetrated as highly when compared with other disciplines such as management. Tablet PC with pen based computing offers a way to solve this problem.

The “Strength of Materials” is a required course that is taken by Civil and Mechanical engineering students in their sophomore year. The course was delivered in the traditional lecture mode with the help of a Tablet PC that was connected to a video projection system. The instructor sat facing the students and wrote the required lecture material on the tablet as if it was being written on a black board. This approach allowed the instructor to face the students during the delivery of the lectures, an improvement over the use of the blackboard. The projection screen served the purpose of the blackboard without obstructing the student’s view from the instructor leading to a better visibility of the lecture material. Using different colors the instructor also had a choice of highlighting the lecture material for added emphasis and clarity. It shows that a combination of Tablet PC with a video projection system provides an improved alternative to a chalk and blackboard.

In the presentation method that uses chalk and blackboard the lecture material is erased after the blackboard is full or at the end of the lecture session resulting in the loss of lecture material. By using the Tablet PC, the entire lecture sessions was saved in the form of a computer file. A course website was created that contained the links to these files pointing to the specific lectures. An example of this approach for creating a course website for the “Strength of Materials” course is provided in reference [2]. The hyperlinks within the course website link to the computer files that were created during lecture presentations. These files have a special format, therefore, to view them the “Windows journal Viewer” a freeware program is used and it can be downloaded from the Microsoft website [3].

The course web site provides anywhere access to the class material on a 24/7 basis to the students who missed the class. It also benefits the students who were present in the class because they can pay more attention to the lecturer’s spoken words due to the availability of lecture notes online after the class is over.

Conclusions and Future Recommendations

The suggested approach provides a method to quickly create live digital lecture presentation material that does not require an instructor to significantly alter his existing mode of teaching. The digital content produced during the lecture can easily be used to create a course website with minimal required skills.

The laptops are becoming very common these days and the cost of a Tablet PC is slightly higher than a regular laptop. A combination of networked Tablet PC with a wireless projection system would eliminate the need to purchase expensive electronic blackboard system. The suggested combination could also be used in a portable mode to convert any regular classroom into an electronic classroom.

The lecture notes created using this process lack the instructor’s voice due to the missing capability of the program to include the digital audio files. There is a need to improve the suggested process to provide a richer multimedia experience.

References

[1] Microsoft Website for Tablet PC, http://www.microsoft.com/windowsxp/tabletpc/default.mspx.

[2] Strength of Material course website, http://www.eng.ysu.edu/~jalam/ceegr2602/.

[3] MS Windows Journal Viewer, http://www.microsoft.com/windowsxp/downloads/tools/default.mspx.

Javed Alam, Ph.D.

Professor

Civil/Environmental/Chemical Engineering Department

YoungstownStateUniversity

Youngstown, Ohio44555

USA

jalam@cc.ysu.edu

A quality approach for collaborative learning scenarios

Introduction

New electronic educative tools aim at supporting collaborative learning activities. When a teacher wants to experiment such an activity, s/he must define and set out clearly the rules: e.g., “The goal of the exercise is to present the topic X, in a structured way. For that purpose, the first step is to form teams in order to collect information. Then, you must select and classify the most interesting information and send it to me. When I agree on the content, you can prepare your presentation.” We call these rules and their sequencing “collaborative learning scenarios”. Most of the time, teachers themselves define these scenarios, but sometimes, didactical organisations (e.g. TECFA) provide us with generic ones [1]. Enacting these scenarios into different contexts or different platforms suggests frequent modifications of the scenarios and therefore an easy way to adjust them. We believe that exchanging such pieces of information will become usual. National programs for sharing the educational resources [2] corroborate this idea, pointing out the quality of collaborative learning scenarios as central in this approach.

The Capability Maturity Model

The quality aspects of e-learning objects are currently being explored, resulting in the definition of “eQuality Learning Standards” [3]. However, these standards qualify educational contents but nothing is said about the qualification of the scenario itself.

The Capability Maturity Model (CMM) was introduced several years ago to characterize organisations through their development process [4] and to assess their quality level. A maturity scale (five levels) is proposed in this model. The general statement is: the more the organisation matures; the better its development process is. These levels are briefly described in figure 1. At the highest level, the process is always reconsidered in order to be optimised.

Figure 1. The five levels of process maturity

A pedagogical scenario involving different participants can be seen as an ideal process where teachers and students act to reach a given goal. The CMM is therefore suited to qualifying the collaborative learning scenarios. According to process assessment rules, we can state that an observation of what is going on during the pedagogical activity is necessary to reach the highest levels.

The different steps for pedagogical scenario improvement

The CMM approach is a long-term approach where the highest quality for pedagogical scenarios can only be reached after their observation during execution. We must consequently follow three main steps to make this quality improvement possible: the modelling of pedagogical scenarios; their enactment; their observation during performance.

Modelling a pedagogical scenario requires a good understanding of the educational process itself: the different sub-activities must be identified, as well as the actors, and the synchronisation between these sub-activities must be clearly established (activity dependence, parallelism). A modelling language could be IMS Learning Design specification [5] since it has been suggested as a flexible way to represent and encode such pedagogical scenarios [6].

Enacting a scenario consists in setting the environment and performing the scenario itself. For instance, the designation of actors (the teacher is John) or the instantiation of tools (the search engine in this context is ‘Google’) are part of the setting phase. Most of the time, this enactment is facilitated by the use of a pedagogical platform.

Observing the scenario consists in gathering information about what is really done in the execution of the pedagogical task. Results obtained from the analysis of the collected information can comfort the teacher in the use of this scenario: every action is realised in accordance with what was expected and the results obtained from the pedagogical scenario execution are acceptable. On the other hand, the analysis may suggest reconsidering the scenario: this is the case when we discover that students always do additional subtasks (not mentioned in the scenario) or skip some recommended subtasks to succeed in their activity.

The observation phase: how to do it?

Gathering information concerning the activities performed by the participants is a difficult task that can be achieved during or at the end of the scenario. Methodologies exist in order to collect and analyse these data at the end of the work through interviews and questionnaires [7]. However, the need to compare the actions completed by the actors with those expected in the scenario led us to use log file analysis [8].

One of the existing difficulties in such an approach is the low level of the logs. They usually represent a basic interface action done by a user. In order to match actions described in the scenario, we must change the level of granularity of the logs, transforming them into more abstract data (e.g. actions). The granularity of the logs can be changed via intelligent techniques like case-based reasoning or pattern recognition.

Current Experiments and Conclusion

At theUniversity of Savoie, we have developed the “electronic schoolbag”, a digital workspace [7]. This online environment is dedicated to the educational world: it offers the pupils, students, teachers, school staff, or parents, personal and group workspaces in which individual or collaborative activities can take place. Today, nearly 20 000 people at the University of Savoie and in French secondary schools use this workspace. New pedagogical practices emerge from the use of the e-schoolbag and we need to establish regulation of the collaborative activity, following pedagogical scenarios.

Increasing the level of quality for pedagogical scenarios requires an observation step. It is possible to ask users to analyse their actions after their performance in an educational scenario. A complementary approach is to observe the users’ actions directly through a log file. The interpretation of these logs is difficult and requires different levels of abstraction on the log files. The analysis at the correct level of what the actors have actually done in the pedagogical scenario provides the teacher with significant indications for reconsidering her/his scenario. The level of maturity of pedagogical scenarios is thus increased.

References

[1] [1] Schneider, D., Dillenbourg, P., Frété, C., Morand, S., Synteta, P. (2003). The TECFA SEED learning activities catalog, retrieved October 15, 2004 from http://tecfa.unige.ch/proj/seed/catalog.

[2] [2] INRP (n.d.) Site de la main à la pâte, retrieved October 15, 2004 from http://www.inrp.fr/lamap/

[3] [3] LIFIA and EifEL (2004). Introducing the Open eQuality Learning Standards, retrieved October 15, 2004 from http://www.qwiki.info/observatory/quality/oeqls/intro.

[4] [4] Paulk, M. C., Curtis, B., Chrissis, M. B., & Weber, C. V. (1993). Capability Maturity Model - Version 1.1. IEEE Software, July, 18-27.

[5] [5] IMS Global Learning Consortium: Learning Design Specification, http://www.imsglobal.org/learningdesign/.

[6] [6] Koper, E. J. R., & Olivier, B. (2004). Representing the learning design of units of learning. Educational Technology & Society, 7 (3), 97-111.

[7] [7] Caron, B., Carron, T., Chabert, G., Courtin, C., Ferraris, C., Martel, C., Marty, J. C., Vignollet, L. (in press). The Electronic Schoolbag: a CSCW workspace. AI & Society.

[8] [8] Heraud, J. M., France, L., & Mille, A. (2004). Pixed: An ITS that guides students with the help of learners’ interaction logs. Paper presented at the workshop in 7th International Conference on Intelligent Tutoring Systems, September 2004, Maceió, Alagoas, Brazil.

Jean-Charles Marty

Syscom Laboratory,

University of Savoie, France

Jean-Charles.Marty@univ-savoie.fr

Jean-Mathias Heraud

Computer Science Department, ESC, Chambéry, France

Thibault Carron

Syscom Laboratory,

University of Savoie, France

Laure France

Computer Science Department, ESC, Chambéry, France

A Case Study by Applying Web-based Laboratory into Natural Science Courses in Taiwanese Elementary Schools

Introduction

This paper presents a case study by applying a web-based virtual lab for teaching natural science in Taiwanese elementary schools. Based on Kolb’s Learning-Style Inventory (1985), in the experimental group, learning achievements of different learning styles are not different significantly. That is, the web-based virtual learning environment is suitable for various learning styles. However, students with the “accommodator” learning style differed most significantly in the learning achievement between the experimental group and the control group. The learning achievement of “accommodator” students in the experimental group is much better than that in the control group. In addition, all students in the experimental group achieved better grades than those in the control group, and most of the students surveyed indicated that they would rather be willing to use the web-based learning environment than read textbooks. The results of the teaching experimentation and questionnaire survey demonstrate the practicality and effectiveness of the web-based learning environment being studied, and thus encourage further development of that environment.

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5PthP IEEE International Conference on Advanced Learning Technologies (ICALT 2005)

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T TStructured, Online Evaluations and the First-Year Design Process

The Project

Learning Goals

Interesting Findings

A Survey

Conclusion and Implications

An Examination of a Comprehensive Preschool Curriculum and its Impact on Early Literacy Skills in Newark, New Jersey

Abstract

Introduction

Sample

Method

Treatment

Result

Main Treatment Effect

Effect of Implementation Quality:

Discussion

References

Introduction

The E-Learning Recommendation

Considerations

References

1. Introduction

2. Designing Learning Objects in Operational Amplifier

3. Conclusion

References

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Introduction

The participants

Interview outcomes

Discussion and conclusions

References

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Educational Background

Rationale For Implementing E-CaD

Student Academic Expectations

Faculty Academic Expectations

Conclusion

References

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Abstract

Introduction

Modes of Collaboration

Transforming the User Experience

Installation and configuration

User Studies

Random Groups

Existing Groups

Conclusion

Acknowledgements

References

Abstract

Introduction

Methodology

Participants

Conclusions

References

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Motivation

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Conclusions

References

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Conclusions

References

The Tablet PC

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5P^th^P IEEE International Conference on Advanced Learning Technologies (ICALT 2005)

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TStructured, Online Evaluations and the First-Year Design Process