Elements of effective instructional design for elementary mathematical problem solving computer software
Notice bibliographique
Résumé
Problem solving is an area of mathematics needing urgent attention by educators. Mathematics organizations, researchers and curriculum strongly advocate for increased problem solving skill development in students. Computer technology may aid some students in learning or practising problem solving skills. This research investigates student perceptions of how effective are instructional design elements of mathematical software, as it relates to their development of problem solving skills. Eighteen instructional design elements were identified from the literature on educational software design. Thirty-two grade five students from varied socio-economic backgrounds in a large district school board in Ontario, Canada, used three mathematical problem solving software programs. For each program, students recorded their perceptions of the effectiveness of each design element in solving mathematical problems, using a written questionnaire and an audiotaped interview. The results of this study indicate that all eighteen identified instructional design elements were judged to be effective by students for learning in mathematical software programs. Students highly rated elements such as feedback, written help and audio guides, because they perceived these elements to aid in their understanding and navigation of the program, as well as provide assistance in persevering with challenging problem solving tasks. Students also felt that having attainable and realistic goals was appropriate. Results sometimes differed according to mathematical ability. For animation elements, low ability students perceived animation to aid in their learning more than high ability students. Low ability students perceived organization and tools in a software program to assist them more in their learning than did the high ability students. Gender did not have any effect on student responses. Examination of the three software programs suggests that they were developed to be gender-neutral. This research indicates that students are aware of software design aspects which aid in their learning, and their insights can provide teachers, software developers and software reviewers with instructional design elements (e.g. narrated text written on screen, constant feedback on progress, detailed graphics, realistic goals) to consider when designing or acquiring educational software.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 tête enseignante, 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 ».