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Scalable Teaching and Learning via Intelligent User Interfaces

Fan, Xiangmin (2017) Scalable Teaching and Learning via Intelligent User Interfaces. Doctoral Dissertation, University of Pittsburgh.

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Abstract

The increasing demand for higher education and the educational budget cuts lead to large class sizes. Learning at scale is also the norm in Massive Open Online Courses (MOOCs). While it seems cost-effective, the massive scale of class challenges the adoption of proven pedagogical approaches and practices that work well in small classes, especially those that emphasize interactivity, active learning, and personalized learning. As a result, the standard teaching approach in today’s large classes is still lectured-based and teacher-centric, with limited active learning activities, and with relatively low teaching and learning effectiveness.

This dissertation explores the usage of Intelligent User Interfaces (IUIs) to facilitate the efficient and effective adoption of the tried-and-true pedagogies at scale. The first system is MindMiner, an instructor-side data exploration and visualization system for peer review understanding. MindMiner helps instructors externalize and quantify their subjective domain knowledge, interactively make sense of student peer review data, and improve data exploration efficiency via distance metric learning. MindMiner also helps instructors generate customized feedback to students at scale.

We then present BayesHeart, a probabilistic approach for implicit heart rate monitoring on smartphones. When integrated with MOOC mobile clients, BayesHeart can capture learners’ heart rates implicitly when they watch videos. Such information is the foundation of learner attention/affect modeling, which enables a ‘sensorless’ and scalable feedback channel from students to instructors.

We then present CourseMIRROR, an intelligent mobile system integrated with Natural Language Processing (NLP) techniques that enables scalable reflection prompts in large classrooms. CourseMIRROR 1) automatically reminds and collects students’ in-situ written reflections after each lecture; 2) continuously monitors the quality of a student’s reflection at composition time and generates helpful feedback to scaffold reflection writing; 3) summarizes the reflections and presents the most significant ones to both instructors and students.

Last, we present ToneWars, an educational game connecting Chinese as a Second Language (CSL) learners with native speakers via collaborative mobile gameplay. We present a scalable approach to enable authentic competition and skill comparison with native speakers by modeling their interaction patterns and language skills asynchronously. We also prove the effectiveness of such modeling in a longitudinal study.


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Details

Item Type: University of Pittsburgh ETD
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Fan, Xiangminxif14@pitt.eduxif14
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWang, Jingtaojingtaow@cs.pitt.edu
Committee MemberHauskrecht, Milosmilos@pitt.edu
Committee MemberLitman, Dianelitman@cs.pitt.edu
Committee MemberMenekse, Muhsinmenekse@purdue.edu
Date: 25 June 2017
Date Type: Publication
Defense Date: 18 January 2017
Approval Date: 25 June 2017
Submission Date: 1 April 2017
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 157
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Human-Computer Interaction; Intelligent User Interfaces; Educational Technology
Date Deposited: 25 Jun 2017 19:03
Last Modified: 25 Jun 2017 19:03
URI: http://d-scholarship.pitt.edu/id/eprint/31133

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