Using Information Technology To Facilitate Student Learning
Bibliographic record
Abstract
The use of technology in education has become critical in today's academic institutions.Colleges and universities worldwide have seen a dramatic increase in the use of technology in the classroom.Zayed University, an academic institution in the United Arab Emirates, has recently built an environment where learning is the main focus.English is used as the language of instruction, however as it is not the students' native language they sometimes have difficulties understanding the course content.Furthermore, students are often too shy to ask questions during class time making it difficult for the instructor to monitor comprehension.To address these issues, instructors in the College of Information Systems are using technology as a basis to create an environment that encourages and facilitates student learning.This environment includes a wired laptop-based campus, an IS curriculum that is driven by learning outcomes, electronic portfolios, and the building of learning communities.Technology facilitates student learning in many ways.As an alternative to face-to face communication, students can use a variety of tools such as electronic mail, Blackboard, Internet and the Intranet, and shared network drives for communication and information access and exchange.Moreover, students are required to develop an electronic portfolio, which includes their most important learning experiences.Using technology, faculty can access and assess student portfolios and provide feedback and guidance online.In addition, the wired campus allows students to create learning communities where ideas, information and knowledge are shared.Faculty can join these communities to integrate multiple learning perspectives as well as provide guidance and learning structures where reflection and critical thinking are encouraged.This new learning environment has resulted in a less inhibited student body where technology is used extensively to interact with other students, faculty members as well as the outside community to seek and create knowledge and ultimately become independent lifelong learners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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 source (direct Gemma or distilled Codex), 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".