A Semi-Automatic Knowledge Discovery Tool to Identify and Visualize Course Bottlenecks
Notice bibliographique
Résumé
Contribution: In this study, we examine the notion of course bottlenecks and their prevalence in various academic courses, particularly those in which a higher-than-average number of students experience failure. Subsequently, we delve into the features of a knowledge discovery software tool that has the capability to identify course bottlenecks and present the findings through a user-friendly graphical interface, accompanied by comprehensible explanations. Background: In many courses, there exist some topics which many students find difficult to comprehend, which we name as course bottleneck. While top and motivated students self-learn those topics with extra effort, remaining students often try to only get a superficial understanding of those topics or skip them altogether. In the end, they fail the course. When analyzing the topics that might have caused a student to fail a course, the information that becomes available is often anecdotal rather than data driven. Besides, what is worse is that the information generally comes late in a semester, if at all. Course evaluations tend to come too late to be of use to the students who report them, and end of semester grades often do not reflect which areas are problematic for students. In fact, course evaluation provides an overall experience of the course without pinpointing the bottleneck of that course. Learning Management Systems focuses on individual student performance level but does not provide any means to identify course bottlenecks. There are many initiatives for identifying curricular bottlenecks, but they do not follow a methodological and understandable approach. In this paper, we fill this void. Intended Outcomes: We discuss a web-based application we have built, which identifies concepts within a course as bottlenecks once given class statistics and course content as input. Such an application will assist teachers and administrators to improve curriculum so that both present and future students are benefited. By reducing curricular bottleneck, this software will make enrolling in a course a better experience and will increase the passing rate thereby reduce attrition. Application Design: By assigning each test question a set of additional tags, which connect it to specific concepts, we are able to track empirical data to identify the concepts which are likely to be bottlenecks. We then consolidate this data with students' grades, consistency, and other factors to provide a numeric score denoting the likelihood for a topic to be course bottleneck. We then present the results with a visual aid to highlight the problematic components in a course in the context of other topics that are covered in that course. Findings: The ongoing implementation of this application at a Midwest University of USA has yielded encouraging outcomes in the identification of course bottlenecks. Currently, our focus lies on transitioning the software from the testing phase to the deployment phase. In addition to refining the core logic of the software, we are actively developing a web platform and graphical user interface (GUI), incorporating features such as access authentication and data entry and storage. Prior to a wider deployment, extensive testing will be conducted across multiple courses on campus. Further research and testing are imperative to enhance and fine-tune the system.
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 | 0,004 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,012 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,003 |
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 ».