NDLI: Do Knowledge-Component Models Need to Incorporate Representational Competencies?

Please wait, while we are loading the content...

Applying a Framework for Student Modeling in Exploratory Learning Environments: Comparing Data Representation Granularity to Handle Environment Complexity

AI in Informal Science Education: Bringing Turing Back to Life to Perform the Turing Test

International Journal of Artificial Intelligence in Education : Volume 27, Issue 1, March 2017

International Journal of Artificial Intelligence in Education : Volume 26

International Journal of Artificial Intelligence in Education : Volume 25

International Journal of Artificial Intelligence in Education : Volume 24

International Journal of Artificial Intelligence in Education : Volume 23

Do Knowledge-Component Models Need to Incorporate Representational Competencies?

Content Provider	Springer Nature Link
Author	Rau, Martina Angela
Copyright Year	2016
Abstract	Traditional knowledge-component models describe students’ content knowledge (e.g., their ability to carry out problem-solving procedures or their ability to reason about a concept). In many STEM domains, instruction uses multiple visual representations such as graphs, figures, and diagrams. The use of visual representations implies a “representation dilemma”: students learn new content from visual representations they may not yet understand at the same time as they learn about visual representations that show content they do not yet understand. Therefore, students’ learning of content knowledge and of representational competencies (i.e., knowledge about representations) is invariably intertwined. Consequently, instruction may need to adapt not only to students’ acquisition of content knowledge but also to their acquisition of representational competencies. This claim corresponds to the hypothesis that knowledge-component models that describe content knowledge and representational competencies should be more accurate than knowledge-component models that describe only content knowledge. Yet, this hypothesis has not yet been tested. The work in this article tests this hypothesis by comparing knowledge-component models that describe representational competencies and content knowledge to knowledgecomponent models that describe only content knowledge. Analysis of log data from two experiments on chemistry learning with overall 203 undergraduate students suggests that including representational competencies into knowledge-component models increases model fit if the representational competencies are difficult. This finding suggests that students can learn abstract content knowledge only if they have a prerequisite level of representational competencies, and that educational technologies should use adaptive knowledge-component models that capture representational competencies the student has not yet mastered.
Starting Page	298
Ending Page	319
Page Count	22
File Format	PDF
ISSN	15604292
Journal	International Journal of Artificial Intelligence in Education
Volume Number	27
Issue Number	2
e-ISSN	15604306
Language	English
Publisher	Springer New York
Publisher Date	2016-12-16
Publisher Institution	International AIED Society
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Connection making Educational Technology User Interfaces and Human Computer Interaction Artificial Intelligence (incl. Robotics) Multiple representations Computers and Education Spatial skills Inductive learning processes Sense-making processes
Content Type	Text
Resource Type	Article
Subject	Education Computational Theory and Mathematics E-learning

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

How Should Intelligent Tutoring Systems Sequence Multiple Graphical Representations of Fractions? A Multi-Methods Study

Designing Grounded Feedback: Criteria for Using Linked Representations to Support Learning of Abstract Symbols

The role of representational competencies for students’ learning from an educational video game for astronomy

Making connections among multiple graphical representations of fractions: sense-making competencies enhance perceptual fluency, but not vice versa

Comparing the effects of representational tools in collaborative and individual inquiry learning

Stupid Tutoring Systems, Intelligent Humans

Twenty Years on: Reflections on “Supporting the Use of External Representations in Problem Solving”…

Reflections on Andes’ Goal-free User Interface

Preface to Special Issue on User Modelling to Support Personalization in Enhanced Educational Settings

Do Knowledge-Component Models Need to Incorporate Representational Competencies?

Similar Documents

How Should Intelligent Tutoring Systems Sequence Multiple Graphical Representations of Fractions? A Multi-Methods Study

Designing Grounded Feedback: Criteria for Using Linked Representations to Support Learning of Abstract Symbols

The role of representational competencies for students’ learning from an educational video game for astronomy

Making connections among multiple graphical representations of fractions: sense-making competencies enhance perceptual fluency, but not vice versa

Comparing the effects of representational tools in collaborative and individual inquiry learning

Stupid Tutoring Systems, Intelligent Humans

Twenty Years on: Reflections on “Supporting the Use of External Representations in Problem Solving”…

Reflections on Andes’ Goal-free User Interface

Preface to Special Issue on User Modelling to Support Personalization in Enhanced Educational Settings

Do Knowledge-Component Models Need to Incorporate Representational Competencies?