Situating Student Learning in Rich Contexts: A Constructionist Approach to Digital Archives Education
Bibliographic record
Abstract
Objective - This paper sought to determine whether a constructionist pedagogical approach to digital archives education could positively influence student perceptions of their learning. Constructionism is a learning theory that places students in the role of designers and emphasizes creating tangible artifacts in a social environment. This theory was used in the instructional design of the Digital Archive Creation Project (DACP), a major component of a digital archives course offered to students enrolled in a Master’s program in library science at Pratt Institute School of Library and Information Science. Methods - Participants were the 31 students enrolled in the DACP during the fall and spring semesters of 2010. They were surveyed as to their perceived learning outcomes as a result of their engagement with the DACP. Results - Results indicated that students perceived strong increases in their learning following their engagement in the DACP, particularly in terms of their skills, confidence, understanding of topics covered in other courses, and overall understanding. Factors that influenced these increases include the collaborative teamwork, the role of the facilitator or instructor, and individual effort. Conclusion - The project demonstrated that a constructionist pedagogical approach to digital archives education positively impacted students’ perceptions of their learning.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".