Role of Learning Management Systems for Formative Assessment in Higher Education
Bibliographic record
Abstract
This study addresses faculty perceptions of how the features of existing learning management systems (LMS) currently and potentially enhance their assessment of student work. Within this type of technology, a selection of a couple of main systems, namely Blackboard and Moodle, was examined. A critical analysis of the exiting literature on the adoption of assessment features within the classrooms for formative purposes is presented. A mixed methods research design was used in order to evaluate the effectiveness of formative assessment tools in LMS. A survey was electronically distributed to professors in two mid-sized universities in Eastern Ontario, Canada to gather data in the first phase. The second phase of data collection entailed interviewing a subset of the professors after analysing the first set of data. Additional analysis was conducted in order to identify the factors that can elevate the perception and use of LMS as major tools for formative assessment. Results revealed that professors are generally struggling with the existing tools in LMS and perceive them to be only marginally effective to conduct formative assessment to the extent that they desire, especially at higher order level. They also consider the existing number of tools as being limited. Recommendations were made to improve the design of assessment tools in LMS for formative purposes. Training was identified as the main factor to increase the use of these tools along with receiving institutional support, extra time and technical help in integrating the new tools in their teaching.
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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 | 0.019 | 0.067 |
| 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 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".