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Enregistrement W4241559733 · doi:10.1145/1269900.1268910

Learning strategies sessions within the classroom in computing science university courses

2007· article· en· W4241559733 sur OpenAlexaffabout
Diana Cukierman, D. Thompson

Notice bibliographique

RevueACM SIGCSE Bulletin · 2007
Typearticle
Langueen
DomaineComputer Science
ThématiqueOpen Education and E-Learning
Établissements canadiensSimon Fraser University
Organismes subventionnairesnon disponible
Mots-clésSession (web analytics)Mathematics educationContext (archaeology)Computer scienceActive learning (machine learning)PsychologyWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

This abstract describes a research project being conducted at a Canadian university in core first year undergraduate Computing Science courses. We are investigating the impact of integrating learning strategies instruction within the regular lab time of these courses to aid students' learning. Our concern, shared by educators and academic advisors, is that some students entering post secondary studies are unfamiliar with effective learning and study strategies. Our sense is that students most needing this support are those with a low grade level average. However, providing learning strategies instruction to these students only may be stigmatizing and counterproductive. Likewise,integrating learning instruction into regular course time may increaseaccessibility and inclusieness for students who are reluctant toseek support even if needed. At the same time this integration may benefit any student, particularly if sessions are short enough. Universities commonly offer generic study skills courses.However, increasingly, and in line with our approach, educatorsare recommending including study strategies instruction within the context of regular academic courses. Some forms of suchintegration include supplemental instruction, adjunct courses anddiscipline-based study skills courses (references are not providedhere due to lack of space). In contrast to these programs, ourlearning sessions are held within regular classes and are tied to course topics. Also, students are required to apply conceptsreviewed during the learning session to course assignments. Aswell, sessions involve both the course instruction and sessionfacilitator allowing for rich discussions about the application of learning theory to course assignments. We describe a representative semester offering in which we have run this project in two courses, including two academic strategies sessions per course. The first session, Learning Strategies, was graded and built into the courses' timetables. A second session, also specifically offered to the students in the courses, addressed stress and time management issues, which appear to interfere in the students' learning process. The Learning Strategies session introduced students to Bloom's taxonomy of learning levels [1] and demonstrated how this model applies to the course topics. Bloom's taxonomy is particularly relevant to the courses selectedfor our study, requiring that students be capable of applying the whole range of learning levels in Bloom's model. As a follow-up, students were required to develop questions at specified Bloom's learning levels, which were selectively included in the course exam. A second component of the Learning Strategies session asked students to consider ways of adapting for tasks of iffering time demands: working outside of class on weekly assignments and writing time-pressured exams. The Stress and Time Management session was optional and did not provide course marks. This session dealt with signs of burn out, strategies to deal with it, and resources students can access to get support. In anonymous and voluntary questionnaires we surveyed students about the usefulness of integrating learning strategies instruction into their courses. In the semester described here there were 140 students in both courses; 59% of the students submitted the questionnaires, 77% attended a Learning Strategies session. Our analysis includes self-reported prior grade level, course commitment level average and level of motivation to attend learning sessions. Survey responses reveal that students who were most motivated to attend these sessions have relatively low prior grades: from C to B. This is in fact our intended audience: students who struggle to improve in their studies. Students with higher grade levels were not as interested. Other analysis showed that those students who have a higher course commitment level are more interested and find these types of sessions more useful. Many students responded that they want more time spent on this type of session and more practical learning advice. We also surveyed students about stress and time management. A much higher percentage of stressed students are found within those reporting intermediate prior grades. Very interestingly, those who declare the lowest commitment level proportionally report being the most stressed. Students assessing themselves lower in time management and study techniques also indicated having low levels of commitment to the course, suggesting that our intervention is worthwhile. So far we have implemented these sessions for three semesters and have analyzed the data from one semester only. However feedback from students and academic advisors confirms our preliminary results that this approach is very promising. Furthermore, our departments are currently considering applying our model to more courses. For the future, we will continue to investigate incorporating these and other learning activities into computing science courses.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,610
Score d'incertitude au seuil0,877

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0020,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,277
Écart entre enseignants0,261 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2007
Routes d'admission2
Résumé présentoui

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