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Grant: Discovery Grants Program - Individual

Carleton University

$41,000.00

Supporting Learning of Programming with Tutoring Systems

EF 2023-24

Résumé
In many institutions, programming classes are now endorsed for all students (not just computer science majors) at pre- and post-secondary levels. However, programming classes have notoriously high failure and drop-out rates. This may be because programming, while sharing some overlap with other problem-solving activities, additionally requires a novel way to think and problem solve in order to master the skills needed to program. Since these skills are unique to programming, supporting them requires innovative pedagogical approaches. My research program will advance educational technologies, called tutoring systems, that help students learn to program by encouraging constructive processing of examples. Examples play a key role in cognitive skill acquisition but to date relatively little work exists on leveraging them to support learning of programming via tutoring systems. To address this gap, the proposed work has three main research streams: (1) interventions focused on example design (including gaps in example solutions, prompts to encourage self-explanation, and erroneous examples), (2) instructional sequencing of examples vs. problems, and (3) promotion of programming as a creative activity through multiple examples. In the short term, the focus will be on the design of tutoring system functionalities, including studies to identify interface features that promote student learning through constructive reasoning. The long-term goal is to add to the tutoring systems adaptation capabilities that tailor instruction, accomplished by extending the tutors with student models. A student model stores information about individual students (e.g., their knowledge and affect) and updates that information in real time based on students' interactions with a tutoring system; this information is used by the tutoring system to tailor instruction to a given student's needs. The proposed research has intellectual merit. The tutoring systems developed by my research program will serve as testbeds for answering research questions related to effective tutoring-system design, as well the utility of novel cognitive and affective interventions focused on core programming skills. Thus, the results will contribute to artificial intelligence & education, cognitive science, and human-computer-interaction fields. On the broad impact side, my research program will produce a series of online tutors designed for a diverse audience that provide personalized help to all students, including ones coming from non-traditional CS backgrounds. By making programming accessible, this has potential to motivate interest in this domain and thus broaden participation in the field.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
RGPIN-2022-04876
Name nom
Muldner, Kasia
Department département
Cognitive Science, Institute of - Cognitive Science, Institute of
Organization id
23
Institution établissement
Carleton University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2022
Award amount
41000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
1210
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Psychology
Area of application fr
Psychologie
Research subject code
9999
Research subject group en
Not available
Research subject group fr
Not available
Research subject en
Non disponible
Research subject fr
Non disponible
Application title
Supporting Learning of Programming with Tutoring Systems
Keyword
AI & Education; cognitives science; data mining and machine learning; educational technologies; human-computer-interaction; personalized learning; programming cognition; tutoring systems
Application summary
In many institutions, programming classes are now endorsed for all students (not just computer science majors) at pre- and post-secondary levels. However, programming classes have notoriously high failure and drop-out rates. This may be because programming, while sharing some overlap with other problem-solving activities, additionally requires a novel way to think and problem solve in order to master the skills needed to program. Since these skills are unique to programming, supporting them requires innovative pedagogical approaches. My research program will advance educational technologies, called tutoring systems, that help students learn to program by encouraging constructive processing of examples. Examples play a key role in cognitive skill acquisition but to date relatively little work exists on leveraging them to support learning of programming via tutoring systems. To address this gap, the proposed work has three main research streams: (1) interventions focused on example design (including gaps in example solutions, prompts to encourage self-explanation, and erroneous examples), (2) instructional sequencing of examples vs. problems, and (3) promotion of programming as a creative activity through multiple examples. In the short term, the focus will be on the design of tutoring system functionalities, including studies to identify interface features that promote student learning through constructive reasoning. The long-term goal is to add to the tutoring systems adaptation capabilities that tailor instruction, accomplished by extending the tutors with student models. A student model stores information about individual students (e.g., their knowledge and affect) and updates that information in real time based on students' interactions with a tutoring system; this information is used by the tutoring system to tailor instruction to a given student's needs. The proposed research has intellectual merit. The tutoring systems developed by my research program will serve as testbeds for answering research questions related to effective tutoring-system design, as well the utility of novel cognitive and affective interventions focused on core programming skills. Thus, the results will contribute to artificial intelligence & education, cognitive science, and human-computer-interaction fields. On the broad impact side, my research program will produce a series of online tutors designed for a diverse audience that provide personalized help to all students, including ones coming from non-traditional CS backgrounds. By making programming accessible, this has potential to motivate interest in this domain and thus broaden participation in the field.