Assessment of a Survey Instrument for Measuring Affective Pathways
Notice bibliographique
Résumé
This research paper analyzes the emotions that students experience while completing ill-defined complex problems called Open-Ended Modeling Problems in their engineering courses.Students are asked to make their own modeling decisions, rather than being given those assumptions, as is the case in most textbook problems.There are many approaches they can take, and having to make decisions and assumptions that impact the problem has been found to generate strong emotions.Goldin's research on mathematics education asserts that students tend toward affective pathways while completing problems.An affective pathway is the sequence of emotions that a student goes through while solving a problem.Goldin theorizes that there are two main categories of affective pathways that students fall into: positive pathways and negative pathways.This paper builds on our previous work on the development of a survey instrument to quantitatively measure affective pathways.The survey asked students to drag and drop emotions into the order they experienced them during their problem solving process.In this study, we sought to improve upon our survey instrument.Based on our previous research, we added several emotions and alphabetized the list to see whether the order of words impacted the responses.Here, we examine the results from an updated survey question as well as a small set of interviews conducted to investigate how students approach answering the survey question by having them think aloud while completing it.The survey was sent to six classes at five universities, and interviews were conducted with six students at two of those universities.Through our analysis, we found that most students feel confused or frustrated at some stage, and that their emotions change as they continue from start to finish, which is in line with the findings of the previous version of the survey instrument.We are looking further into whether the students turned their frustrations into the positive or negative pathways that Goldin describes.From the interviews, we found most of the verbalized pathways matched what was submitted through the survey instrument.However, there were instances where the submitted and verbalized pathway did not match, suggesting further changes to the question's implementation.Developing a reliable method for measuring affective pathways will enable future study of why and when positive or negative pathways occur, as well as potential actions that engineering educators can take to help students interrupt negative pathways.Goldin's work suggests that negative pathways influence students' global affect, which could impact retention in engineering.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,053 | 0,091 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».