Prioritizing the implementation of e-learning tools to enhance the learning environment
Bibliographic record
Abstract
The implementation of both blended learning and web-based programs is becoming more prevalent within higher education in Canada. Thus, there is increasing reliance upon e-learning tools to support student learning in a variety of teaching environments. Typically, budgetary and programmatic decisions regarding the investment of e-learning resources are made by information technology (IT) administrators and/or professors. However, students are the primary beneficiary of most IT investments. This study examined Business and IT students’ usage patterns and perceptions related to a suite of e-learning tools through open ended and Likert scale survey questions. Findings will assist post-secondary decision-makers in prioritizing IT investments. Further, study results will ensure that the design and implementation of e-learning tools address the needs and usage patterns of the primary client—the student. Resume L’implantation des programmes d’apprentissage hybride et bases sur le Web devient de plus en plus courante dans les etudes superieures au Canada. Donc, il y a une plus grande utilisation des outils eLearning pour soutenir l’apprentissage etudiant dans une variete d’environnements d’enseignement. Typiquement, les decisions touchant les budgets et les programmes concernant l’investissement de ressources en eLearning sont prises par les administrateurs des services de technologie de l’information (IT) et/ou des professeurs. Cependant, les etudiantes et les etudiants sont les premiers beneficiaires de la plupart des investissements en IT. Cette etude examine les profils d’utilisation d’etudiants en commerce et en technologie de l’information des outils eLearning mis a leur disposition, grâce a des questions ouvertes et a un sondage utilisant une echelle de Likert. De plus, les resultats de l’etude permettront de s’assurer que le design et l’implantation d’outils eLearning repondent aux besoins et au profil d’usager du premier concerne, l’etudiant.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".