Bibliographic record
Abstract
<p>Although a number of claims have been made describing massive open online courses (MOOCs) as a disruptive innovation in education, these claims have not yet been proven through research. Instead, MOOCs should perhaps be considered as an integrative model for higher education systems, but to do so will require recognition of credentials. Initial experiments of MOOCs were not offer academic credit, but recently there have been some attempts to offer course credit for MOOCs or MOOC-like courses. However, does earning a credit will affect students’ performance and behavior in MOOCs has not been explored closely. Therefore, the aim of this study is to assess the effect of crediting on students’ achievement, perceived intrinsic and extrinsic goal orientations, and perceived course value. A causal comparative research design was applied. Data was collected via 516 responses to an online survey and achievement tests. Three credit conditions were compared: credit bearing, non-credit bearing, and credit careless. ANOVA results showed a significant difference between the credit bearing groups and non-credit bearing groups for all dependent variables. The credit bearing group also scored significantly higher achievement scores than the credit careless group. Credit clearly and significantly affected all dependent variables investigated in this study. Therefore, various possible models can be adopted by higher education institutions to integrate MOOCs as a credit. Further studies can explore the effects of credit on students’ online behaviors, such as engagement with online activities and user events on MOOC platforms.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".