Category Archives: Learning Analytics

#HWL at STEM 2014

STEM2014-logo flowers

Researchers on the How We Learn (HWL) team are presenting in a symposium this afternoon at 3:00 at the STEM 2014 Conference.

Design and Engineering Cognition and
Design-Based Research

Stephen Petrina, Franc Feng, Mirela Gutica, Peter Halim, Yu-Ling Lee, Lesley Liu, PJ Rusnak, Yifei Wang & Jennifer Zhao

University of British Columbia

Symposium Chairs: Yifei Wang & Stephen Petrina

This symposium aims to generate discussion and understanding of design-based research (DBR) in design and engineering cognition. Seven empirical reports exploring design and engineering cognition or using DBR give the symposium depth and structure: Studies of 1) thirty tweenage girls in designing a mother’s day game, media, and robots; 2) fifteen elementary students testing a new educational video game; 3) nineteen young adults within an immersive virtual environment; 4) four teen students on the design of games; 5) six nursing students involved in a simulated learning environment; 6) Conceptual paper exploring technology and the “design” in DBR; and 7) Methodological paper connecting DBR with design and engineering cognition and ethical know-how. Arguably, new technologies along with a return of DIY or maker culture invite or configure everyone to employ inventive practices or “designerly ways of knowing.” Design now marks interaction with new technologies, making DBR increasingly important and relevant for STEM.

Invitation to Mirela Gutica’s PhD Defense

Designing Educational Games and Advanced Learning Technologies:
An Identification of Emotions for Modeling Pedagogical and Adaptive Emotional Agents

Mirela Gutica

Abstract: Emotional, cognitive, and motivational processes are dynamic and influence each other during learning. The goal of this dissertation is to gain a better understanding of emotion interaction in order to design Advanced Learning Technologies (ALTs) and Intelligent Tutoring Systems (ITSs) that adapt to emotional needs. In order for ITSs to recognize and respond to affective states, the system needs to have knowledge of learners’ behaviors and states. Based on emotion frameworks in affective computing and education, this study responds to this need by providing an in-depth analysis of students’ affective states during learning with an educational mathematics game for grade 5-7 (Heroes of Math Island) specifically designed for this research study and based on principles of instructional and game design.

The mixed methodology research design had two components: (1) a quasi-experimental study and (2) affect analysis. The quasi-experimental study included pretest, intervention (gameplay), and posttest, followed by a post-questionnaire and interview. Affect analysis involved the process of identifying what emotions should be observed, and video annotations by trained judges.

The study contributes to related research by: (1) reviewing sets of emotions important for learning derived from literature and pilot studies; (2) analyzing inter-judge agreement both aggregated and over individual students to gain a better understanding of how individual differences in expression affect emotion recognition; (3) examining in detail what and how many emotions actually occur or are expressed in the standard 20-second interval; (4) designing a standard method including a protocol and an instrument for trained judges; and (5) offering an in-depth exploration of the students’ subjective reactions with respect to gameplay and the mathematics content. This study analyzes and proposes an original set of emotions derived from literature and observations during gameplay. The most relevant emotions identified were boredom, confidence, confusion/hesitancy, delight/pleasure, disappointment / displeasure, engaged concentration, and frustration. Further research on this set is recommended for design of ALTs or ITSs that motivate students and respond to their cognitive and emotional needs. The methodological protocol developed to label and analyze emotions should be evaluated and tested in future studies.

When: March 17, 2014 @ 9:00 am
Where: Faculty of Graduate and Postdoctoral Studies, UBC

Using Learning Analytics to Understand the Design of an Intelligent Language Tutor

Using Learning Analytics to Understand the Design of an Intelligent Language Tutor– Chatbot Lucy

Yifei Wang & Stephen Petrina

Abstract—the goal of this article is to explore how learning analytics can be used to predict and advise the design of an intelligent language tutor, chatbot Lucy. With its focus on using student-produced data to understand the design of Lucy to assist English language learning, this research can be a valuable component for language-learning designers to improve second language acquisition. In this article, we present students’ learning journey and data trails, the chatting log architecture and resultant applications to the design of language learning systems.