EvalMe: Exploring the Value of New Technologies for In Situ Evaluation of Learning Experiences
Bibliographic record
Abstract
Tangible interfaces have much potential for engendering shared interaction and reflection, as well as for promoting playful experiences. How can their properties be capitalised on to enable students to reflect on their learning, both individually and together, throughout learning sessions? This Research through Design paper describes our development of EvalMe, a flexible, tangible tool aimed at being playful, enjoyable to use and enabling children to reflect on their learning, both in the moment and after a learning session has ended. We discuss the insights gained through the process of designing EvalMe, co-defining its functionality with two groups of collaborators and deploying it in two workshop settings. Through this process, we map key contextual considerations for the design of technologies for in situ evaluation of learning experiences. Finally, we discuss how tangible evaluation technologies deployed throughout a learning session, can positively contribute to students’ reflection about their learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".