A proposed framework for an adaptive learning of Massive Open Online Courses (MOOCs)
Bibliographic record
Abstract
The use of Massive Open Online Courses (MOOCs) system has increased significantly in the recent years. Among the pioneers of the MOOCs is the Massachusetts Institute of Technology (MIT). The latest development, that of the Internet (including very recently the mobile Internet), has similarly been adopted by many existing higher education providers but has also supported the emergence of a new model dubbed a massive open online course (MOOCs), the term coined in 2008 to describe an open online course to be offered by the University of Manitoba in Canada. A range of both topics and platforms have since emerged and the term was described as "the educational buzzword of 2012" by Daniel (2012) reflecting widespread interest in the concept. MOOCs attract many learners from all over the world, so there is a need to enhance the MOOCs to meet the individual needs. This paper investigates the MOOCs system by reviewing the available literatures and suggesting a proposed framework, which considered a list of recommendation of instructional material using the learner's profile and experience. In this suggested framework, we customize what best requirements and list of recommendations we gain by the learner experience with the system that will be authorized by the teacher assistants and accepted by the professor "Authors". We also utilized the adaptability (UCD); approach which calls for placing the learner at the center of the design process during learners' interactions with the MOOCs system. Moreover, the framework can present the user with a suggested learning requirements to meet the appropriate learning objectives based on their current preferences and experience. As the learner progresses, further recommendations can be made with appropriate resources to enhance and develop the learner's understanding of the previous topics. The framework is open for learners to be evaluated by adapting the existing MOOCs at their institutions, allowing comparison of a variety of aspects including choice of learning path, and learner satisfaction.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".