The Lost Generation in E-Learning: Deep and Surface Approaches to Online Learning
Bibliographic record
Abstract
This study examined the independent effects of six approaches to learning in online computer conferencing: (1) learning: (2) comprehension learning; (3) relating ideas; (4) surface learning; (5) syllabus boundness; and (6) achievement motivation. Deep learning, comprehension learning, and relating ideas were combined into a more general index called meaning or deep approach to Syllabus boundness and surface learning were combined into a reproducing orientation index, a surface approach to learning. Online surveys were conducted in 3 school years at a Canadian university using the FirstClass proprietary software, which had been customized into an online conferencing system. The questionnaire was completed by 114, 280, and 679 students in the 3 years. Factor indexes were created of approaches to learning, active use of online conferencing, the subjective valuation of its personal importance to students, and embarrassment or anxieties over posting messages to online course conferences. Seven hypotheses were developed, the main one being that a approach to learning would result in greater use and personal importance of registered than unregistered course conferences. Only partial support was found for the hypotheses. The approach to learning resulted in a heightened active use of almost all aspects of online conferencing, increased reading and sending of messages, a greater subjective valuation by learners of the importance of participation in conferencing and nonacademic social debates, and a reduction in anxiety about postings. About 15% to 25% of the samples formed a lost generation in the elearning world. These students scored high on a surface approach to learning and low on a approach. Implications for educators who want to reach these students are discussed. (Contains 32 tables and 45 references.) (SLD) Reproductions supplied by EDRS are the best that can be made from the original document. The Lost Generation in E-Learning: Deep and Surface Approaches to Online Learning Carl J. Cuneo), Delsworth Harnish2 831.d Annual Meeting of the American Education Research Association Division C: Investigations of online learning environments New Orleans April 2nd, 2002 Dr. Carl Cuneo, Professor, Sociology Director, EvNet, Network for the Evaluation of Education and Training Technologies http://evnetcanada.org/ Email: Carl.CuneoRLearnLink.mcmaster.ca KTH 608, McMaster University, Hamilton, Ontario, Canada L8S 4M4 Ph. 011 (905) 525-9140, ext. 23602; Fax: 011 (905) 628-3545 2 Dr. Delsworth Harnish Professor and Assistant Dean Bachelor of Health Sciences Health Sciences Centre Room 1J11 1280 Main Street West Hamilton, Ontario L8N 3Z5 E-mail: harnishd@mcmaster.ca http://www.fhs.mcmaster.ca/bhsc/ 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".