Effects of self-controlled learning on self-efficacy and intrinsic motivation
Bibliographic record
Abstract
Motor learning is enhanced by providing learners with control over aspects of their practice conditions (Janelle et al., 1997). However, the underlying mechanisms responsible for these learning effects remain unclear. The purpose of this research was to examine whether self-efficacy and intrinsic motivation help to explain the benefits of self-controlled learning. Participants were given the control to schedule their self-observation video feedback while learning double-mini trampoline progressions. Participants were assigned to a self-control group (n = 15) or yoked group (n = 15). Data collection consisted of two days of acquisition followed by a retention phase 24 hours later. A self-efficacy scale was administered at each acquisition and retention day. Four subscales from the Intrinsic Motivation Inventory (IMI) were measured at the conclusion of each day. Self-efficacy scores for the self-control and yoked group did not significantly differ. When combining the four subscales of the IMI, results revealed a main effect for group, F(1, 28) = 11.41, p = .002, ?2 = .903, with the self-control group feeling more intrinsically motivated to perform the progressions compared to the yoked group. Significant differences were in fact found between the self-control and yoked group for each of the four subscales. Findings suggest that a self-controlled learning environment fosters greater intrinsic motivation. Discussion will focus on the practical implications of these findings.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".