Web-Delivered Supplemental Instruction: Dynamic Customizing of Search Algorithms to Enhance Independent Learning for Developmental Mathematics Students.
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Notice bibliographique
Résumé
Abstract Traditional peer-to-peer Supplemental Instruction (SI) was introduced into higher education over a quarter of a century ago and promptly became an integral part of the developmental mathematics curricula in many senior and community colleges. Later, some colleges introduced Video-based Supplemental Instruction (VSI) and, in recent years, Web-delivered Supplemental Instruction (WdSI), enhancing the delivery of SI. A major shortcoming of these new approaches is the fixed content of a prerecorded SI and its restricted availability. Furthermore, frequent changes in curricula and new or revised textbooks can quickly make SI incomplete. This research suggests a novel approach to a Webdelivered SI (WdSI), where the content and elucidation level of the SI are based on the user-supplied topic description and are precisely selected from an enormous, continuously expanding and constantly available Internet collection. A meta search engine with dynamically adjustable search algorithms was designed and used to conduct experiments; the results demonstrated a high quality of on-demand delivery of relevant SI for any discipline or subject matter. 1. Introduction Peer-to-peer Supplemental Instruction is an in-school tutorial service whose objective is to help students with comprehension and retention of course content. SI is conducted as an informal peer-lead discussion group or lab and is designed to assist and encourage the student to develop courserequired expertise. SI was introduced into higher education over a quarter of a century ago and under different names and disguises promptly became an integral part of the developmental mathematic curricula in many senior and community colleges (Congos & Schopes, 1993). As academic philosophy began stressing more learning and less teaching, educators from the University of Missouri (UMKC) introduced a new technique of bringing SI to students in need - student centric interactive Video-based courses with Supplemental Instruction (VSI), which delivers affordable, effective instruction on the University campus and at remote sites (Martin & Arendale, 1998). Data collected in 1992-1995 by the National Association for Developmental Education (NADE, 1998) supports widespread use and success of this approach. Extensive use of the Internet by the schools and student population commenced a new SI delivery mechanism - Web-delivered SI (WdSI). High availability and ease of access and use made it a favorite with students, allowing for flexibility in topic selection, scheduling participation and selecting a location of delivery. Attempting to fill a void, schools and publishers developed a variety of educational material available on the Web (Nipport, 2001; Progress, 2001; Takle & Taber, 1996). New advances in the delivery technology are hampered by the fixed content of SI. Existing, traditionally delivered courses are either videotaped or transcribed and then saved on the Web. While benefits of this material are vast, they are still limited by frequent changes in the curricula that rapidly make the prerecorded SI incomplete, irrelevant or obsolete. To alleviate the quick obsolescence of the existing Web-delivered SI and to adjust to the fast evolving world of higher education, we propose to adapt results of our earlier research into long query information retrieval (Shapiro & Taksa, 2003). Our research and experiments demonstrated that the quality of information retrieval depends on the completeness and accuracy of the user-formulated query. This could be best accomplished by allowing the user to speak the mind - use natural language to formulate the search query. 2. Using Natural Language to Express Users ' Information Needs There are different types of information needs which, in turn, lead to different ways of expressing these needs as search queries. For some types of information needs a query might be only a few terms long, while other types of information needs will require much longer queries. …
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.
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 | 0,001 | 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 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle