Learning linkages: Integrating data streams of multiple modalities and timescales

Human‐Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania WestEd, San Francisco, California Department of Applied Linguistics and English as a Second Language, Georgia State University, Atlanta, Georgia Department of Psychology, Arizona State University, Tempe, Arizona Learning Sciences Department, Northwestern University, Evanston, Illinois Correspondence Ran Liu, Carnegie Mellon University, Pittsburgh, PA. Email: ranliu@gmail.com Funding information National Science Foundation; Division of Research on Learning in Formal and Informal Settings, Grant/Award Numbers: 1417997, 1418020, 1418072 and 1418181; Institute of Education Sciences, Grant/Award Numbers: R305A100069 and R305A170049

[1]  Paulo Blikstein,et al.  Multimodal learning analytics , 2013, LAK '13.

[2]  Vincent Aleven,et al.  Predicting Student Performance In a Collaborative Learning Environment , 2015, EDM.

[3]  Manu Kapur,et al.  A further study of productive failure in mathematical problem solving: unpacking the design components , 2011 .

[4]  K. Koedinger,et al.  Fostering the Intelligent Novice: Learning From Errors With Metacognitive Tutoring , 2005 .

[5]  Danielle S McNamara,et al.  The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion , 2015, Behavior Research Methods.

[6]  Kenneth R. Koedinger,et al.  Learning Factors Analysis - A General Method for Cognitive Model Evaluation and Improvement , 2006, Intelligent Tutoring Systems.

[7]  Vincent Aleven,et al.  Authoring Tools for Collaborative Intelligent Tutoring System Environments , 2014, Intelligent Tutoring Systems.

[8]  Marcelo Worsley,et al.  Multimodal Learning Analytics as a Tool for Bridging Learning Theory and Complex Learning Behaviors , 2014, MLA@ICMI.

[9]  Scott A. Crossley,et al.  Automatically Assessing Lexical Sophistication: Indices, Tools, Findings, and Application , 2015 .

[10]  Arthur C. Graesser,et al.  Computational Analyses of Multilevel Discourse Comprehension , 2011, Top. Cogn. Sci..

[11]  Michelene T. H. Chi,et al.  Commonsense Conceptions of Emergent Processes: Why Some Misconceptions Are Robust , 2005 .

[12]  Ryan S. Baker,et al.  Educational Data Mining and Learning Analytics , 2014 .

[13]  Tom Routen,et al.  Intelligent Tutoring Systems , 1996, Lecture Notes in Computer Science.

[14]  Andrew D. Ho,et al.  Changing “Course” , 2014 .

[15]  Danielle S McNamara,et al.  Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis , 2017, Behavior research methods.

[16]  Vincent Aleven,et al.  Using an Intelligent Tutoring System to Support Collaborative as well as Individual Learning , 2014, Intelligent Tutoring Systems.

[17]  Vincent Aleven,et al.  Sense Making Alone Doesn't Do It: Fluency Matters Too! ITS Support for Robust Learning with Multiple Representations , 2012, ITS.

[18]  Georg Rasch,et al.  Probabilistic Models for Some Intelligence and Attainment Tests , 1981, The SAGE Encyclopedia of Research Design.

[19]  Vincent Aleven,et al.  Intelligent Tutoring Systems with Multiple Representations and Self-Explanation Prompts Support Learning of Fractions , 2009, AIED.

[20]  Marcelo Worsley,et al.  Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks , 2016, J. Learn. Anal..

[21]  Ran Liu,et al.  Beyond Log Files: Using Multi-Modal Data Streams Towards Data-Driven KC Model Improvement , 2016, EDM.

[22]  Hans Spada,et al.  6 – The Assessment of Learning Effects with Linear Logistic Test Models , 1985 .

[23]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[24]  Manu Kapur,et al.  Productive failure in learning the concept of variance , 2012 .