INVESTIGATING DIFFERENCES BETWEEN INSTRUCTOR EXPECTATION AND STUDENT WORKLOAD IN UNDERGRADUATE ENGINEERING
Notice bibliographique
Résumé
For the past two years at the University of X, first-year engineering undergraduate students have been asked to fill out workload questionnaires. These questionnaires were sent to random samples of the first-year class weekly, where they were prompted to answer questions regarding how much time they devoted outside of the classroom to each particular class. Workload data for 2018 and 2019 showed upward of 30 hours of work outside of the classroom, after the first few weeks of classes once major assignments and examinations began. Evidence in the literature [1,2] suggests that university students face a shortage of time, specifically with first-year students lacking the essential time management skills to be efficient. In the present study, we aim to find a correlation between how long the first-year engineering students spend on a class each week versus how long instructors anticipate the average student would spend on their respective class. In order to do so, we examined the data gathered for 2018 and 2019 fall terms from each student for a specific class and week. Furthermore, additional relevant information will be gathered from the instructors and course coordinators to obtain an estimate on how many anticipated hours a student would have to spend on a class each week versus how long instructors anticipate the average student would spend on their respective class. In order to do so, we examined the data gathered for 2018 and 2019 fall terms from each student for a specific class and week. Furthermore, additional relevant information will be gathered from the instructors and course coordinators to obtain an estimate on how many anticipated hours a student would have to spend on their course that week, given what assessments are in that week. Through analyzing multiple courses, we expect to find a relationship that would suggest whether the hours students spend on assignments is less than, equal to, or greater than what instructors expect for first-year engineering students at University of X to spend. The outcome of this analysis would be beneficial to understand the workloads as perceived by professors and experienced by first-year engineering students. Furthermore, it can highlight potential misjudging of difficulty of each course and assignment, helping instructors to update their expectations and propose fair deadlines and grades for assessments. It can also assist program coordinators to distribute major assessments better towards a steadier and more manageable workload for the students. The students can also benefit from the findings to understand their time commitments.
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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 | 0,003 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».