Examining Young Learners’ Emotions and How They Relate to Cognition and Learning
Bibliographic record
Abstract
Since the mid-2000s, researchers have embraced a dynamic and multidimensional view of learners, where their cognitive, social, and emotional processes are considered, in addition to their background and experiences that they bring to learning. Together these factors influence students in complex and multifaceted ways, and while tackling all of these interrelated components is impossible, in-depth analyses of contributing factors help elucidate their unique influence. The current study more specifically examines emotion in education. Research on emotion has traditionally focused on anxiety associated with test performance; however, the construct of emotion is multidimensional and developmentally sensitive. There is a dearth of research on the impact of emotion among young learners’, particularly regarding associations within the domains of language and literacy. The present study examined the relationship between task types and young learners’ emotional experience during task performance, examined whether this relationship is further differentiated by the learners’ background characteristics, and further considered the temporality of emotion over time across learning tasks. Students engaged in a comprehensive, multi-task, online learning-oriented assessment platform, Speak and Solve (SnS), targeting oral language and cognition. Multi-channel methods of self-reported emotion and behaviour observation data were used to examine the role of affect in cognitive and linguistic processing for 154 grade 4-6 students engaged in SnS. More specifically, the study utilized latent class analysis, multinomial logistic regression, and latent transition analysis to understand what emotional characteristics are associated with different tasks, what predictive factors (demographic, performance measures) are associated with emotion classes, and the stability of emotion trajectories across tasks. Findings highlight the integrated role that emotions play in young learners’ educational experiences. Additionally, results suggest that while most students’ anxiety peaked prior to and at the beginning of the platform, students that were language learners had anxiety that persisted across tasks. Furthermore, student’s motivational learning orientation appeared to play an important role in emotions experienced during SnS. Findings are contextualized and interpreted using the Control-value theory of achievement emotions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".