Science teaching and learning: Exploring barriers by science students with learning disabilities and their science instructors in a CEGEP setting
Notice bibliographique
Résumé
Science is for all individuals regardless of their gender, cultural background, social circumstances, or career aspirations. Yet, not all students are offered equal opportunities to perform well in science. Students with learning disabilities (LD) are continuously lagging behind in science, and scoring significantly lower grades as compared to their typically achieving peers across various settings. Research studies aiming at investigating the difficulties faced by postsecondary students with LD in learning science are sparse. It is critical that barriers experienced by students with LD are explored in order to design and implement interventions for their academic success in STEM programs. The objective of this manuscript-based dissertation is to explore the difficulties encountered by postsecondary science students with LD in engaging with, and learning science. Moreover, this research study investigates the barriers encountered by college science instructors in teaching students with LD. This dissertation draws on a qualitative research approach and comprises three interrelated manuscripts exploring the barriers encountered by students with LD in learning science, and difficulties experienced by science instructors in teaching science to students with LD at Mountain CEGEP. Drawing on Bronfenbrenner’s ecological model, this dissertation offers a comprehensive examination of the interconnections between within-individual and environmental barriers faced by college instructors and students with LD in science education. Rooted in autoethnography, the first manuscript explores my perspectives as a special needs educator and CEGEP biology instructor working with science students with LD and their instructors. Based on my interactions with students with LD, I document the challenges that they encountered in the CEGEP setting. I also share my views on the struggles faced by college science instructors to enact an inclusive environment for their students with LD. Manuscript one also critically analyzes and reflects upon dominant disability frameworks (i.e., medical and social models of disability) by drawing on my journey as a practitioner-researcher. Recognizing the limitations of both the medical and social models of disability in this analysis, I discuss the importance of drawing on Bronfenbrenner’s (2005) ecological model to inform my practice in supporting students with LD and to conceptualize my doctoral thesis. Manuscript two investigates the perspectives of 18 CEGEP science instructors on the challenges that they face while teaching students with LD both inside and outside of their classrooms. From the analysis of interviews, three overarching barriers emerged which include: instructors’ insufficient knowledge and skills in teaching and supporting students with LD; lack of support in working with students with LD; and their difficulty in establishing relationships with students with LD. Lastly, manuscript three examines the views of 11 CEGEP science students with LD on their difficulties in learning science. In addition to participating in semi-structured interviews, 5 of the 11 students participated in a photovoice project. Not only did they photograph artefacts and spaces that represented barriers they encountered in learning science, but they also engaged in writing journals and participating in individual semi-structured interviews for the photovoice project. Analysis of data revealed that students with LD faced these barriers: learning difficulties due to their respective disabilities; perceptions of being academically disadvantaged in comparison to their peers; fast pace of instruction; undifferentiated teaching approaches; and lack of consistency and structure in teaching approaches. Altogether, the findings from these three interconnected studies offer multiple perspectives on the barriers faced by students with LD and science instructors in science education
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 | 0,006 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,013 | 0,007 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,002 | 0,013 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 ».