The Evaluation of an Integrated Growth & Goals Module to Better Equip Students with Learning Skills in Postsecondary Courses: Systematic, Scalable, and Explicit
Bibliographic record
Abstract
In this dynamic and rapidly changing world, students need to be able to continually learn and adapt throughout their lives. However, most students spend years in formal education settings without being explicitly taught how to learn effectively. To reach our goal of explicitly and effectively equipping all students with learning skills, we developed and evaluated a Growth & Goals module. The module is an Open Education Resource for postsecondary students that educators integrate in their courses to teach core learning skills of metacognition, goal-setting, growth mindset, and mindfulness. Over 5000 students at ten institutions have now used the module. In the present study, we evaluated the module using a Practical Participatory Evaluation approach and the 4-level Kirkpatrick Evaluation model. To answer ten questions aligned with the Kirkpatrick model, we collected data from 1845 students and 5 educators from nine undergraduate courses in science, engineering, and mathematics, which were used to investigate ten research questions aligned with Kirkpatrick’s four evaluation levels. For Level 1 (Reaction), students and educators reported high satisfaction and gave constructive suggestions that centred on expanding the module. The training was new to 88% of students. Most completion rates were over 75% when professors provided an incentive (³ 1%). Students in some demographics used the module less than others: lower-achieving, first-generation university students, from outside the Ottawa-Gatineau area, male, and in certain programs. In Level 2 (Learning), students’ metacognitive skills increased throughout the semester. They could identify SMART goals (Specific, Measurable, Accountable, Reachable, and Time-specific) and differentiate growth/fixed mindset statements. At Level 3 (Behaviour), students applied the module within the originating course, indicated their intent to use the module in the future, and a survey of a subsample indicated that most students used or intended to use the module in a new course. Most educators created course-level learning outcomes for the first time to integrate with the module. As an Open Education Resource with a nearly complete “plug-and-play” format, using the module required little time and low technological skills of students and educators; however, greater support, incentives, and rewards could be provided. Research and development require sustained resources. Finally, in Level 4 (Results), educators have used the module in courses in a number of disciplines, including sciences, engineering, mathematics, education, and psychology. The module addresses institutional goals of transformational learning and agility, as well as two provincial degree level expectations that are rarely explicitly taught in courses. In summary, the Growth & Goals module explicitly teaches core learning skills in a way that is systematic, scalable, and explicit for science, engineering, and mathematics courses, with a potential to expand to any discipline.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".