Laboratory and Real-Life Research Advance Intelligent Choices

Learning Sciences & Technologies (LST) graduate students follow the WPI method of integrating theory with practice on campus and in urban classrooms, researching the impact of educational technology with input from teachers and students whose learning experiences—and futures—will be enhanced by these efforts. Faculty and students collaborate with the Worcester Public Schools, applying and testing theories in computer science, cognitive and educational psychology, and statistics in city classroom settings.

Working closely with undisputed leaders in educational psychology and educational data mining, as well as faculty at the cutting edge of intelligent tutoring, cognitive psychology, and artificial intelligence, LST graduate students focus on methods to increase success in STEM (science, technology, engineering, and mathematics) learning and teaching in the United States in grades K-12. 

Researchers use the following resource centers:

Along with these laboratories, LST cognitive models are used to study student thought and learning patterns to design educational materials, practices, and technologies. Topics include instructional technologies, learning with visualizations and simulations, learner characteristics, human-computer interaction, and machine learning.


Advanced Learning Technologies Research Lab

We create, explore, and analyze the impact of Learning Technologies for STEM focusing on various aspects of human learning and incorporating data from myriad sources and equipment.

Our research spans many areas, including Personalized Learning, Affect and Motivation, Metacognition, Embodied Cognition and Wearable Devices for Active Learning, Educational Games, Intelligent Pedagogical Agents, Students with Learning Disabilities, Cultural Differences in the Design and Implementation of Learning Technologies, and Learning Technologies for the Developing World.

Professor Arroyo

Educational Psychology and Mathematics Learning Lab

Teaching and learning mathematics is a highly complex social, exploratory, and creative process. We design novel dynamic technologies that make “math come alive” (Graspable Math, From Here to There!) and use eye tracking, mouse gestures, and log files to explore the coordination of attention, cognition, gestures, and strategies when solving mathematical equations. 

We also use a variety of applied multilevel quantitative methods, observational measures, and assessments to examine the efficacy of instructional, social, and emotional classroom interventions that can improve K-12 math teaching, learning, and engagement. 

Professor Ottmar

Educational Data Mining Research Group Predicts Outcomes

Large datasets of students’ fine-grained interactions (e.g., student S answers math problem X with answer Y at time T) with intelligent tutoring systems, educational interventions, and massive open online courses (MOOCs) help us explore and improve how learners learn and how teachers teach. 

By harnessing methods from machine learning—such as probabilistic graphical models, Markov chains, and deep neural networks—we can develop more accurate predictors of which and when students will succeed, fail, persist, or need help. These predictors can, in turn, serve as the basis for both human-assisted and automated interventions to improve learning outcomes and the personalization of learning.

Professor Beck, Professor Heffernan, Professor WhitehillProfessor Arroyo

Running Classroom Experiments on the Web

Using various web-based platforms and technologies (i.e., ASSISTments, MathSpring, GraspableMath), this group conducts more than 100 randomized-controlled trials in K-12 mathematics classrooms annually. These studies help us understand “what works” with regards to different pedagogical techniques, content, feedback, and tasks, and helps us develop a better understanding of the mechanisms guiding learning. There are a set of methodologic issues that their research group grapples with related to student-level randomized controlled assignment.  

Professor Heffernan, Professor Beck, Professor Ottmar, Professor Arroyo, Professor Whitehill

Machine Perception of Human Learning Group

This group uses machine learning and computer vision to study how learners learn and how they emote while they learn. Particular interests include the training of deep neural networks to recognize students’ facial expressions during learning tasks, and the development of real-time cyberlearning systems that respond instantaneously to learners’ current cognitive, affective, and linguistic needs.

Professor Whitehill, Professor Heffernan, Professor Beck

Embodied Cognition In Mathematics Research Group

This research group carries out research about new ways of learning, using motor actions as well as cognitive thought. We investigate how children may better learn mathematics while exploring the physical space, getting a different understanding of math learning by gesturing, and using technology to guide them through 3D spaces. 

Professor Arroyo, Professor Ottmar

Quantitative Methods in the Learning Sciences

This research group focuses on rigorous quantitative methods such as hierarchical linear models (which is a typical method to use when students are nested inside teachers and teachers are nested inside schools). Other topics include timely learning science issues like structural question modeling, longitudinal data analysis, propensity score matching, regression discontinuity designs, quasi-experimental designs, and advanced topics like principal stratification. Faculty in this group apply (and adapt) statistical methodologies to solve the problems they are working on. 

Professor Somasse, Professor Ottmar, Professor Zou, Professor Heffernan

ASSISTments and Assessment for Retention

Computer science is the foundation for research in expanding, proving, and improving web-based ASSISTments, an intelligent tutoring device, and for educational data mining to maximize functionality in learning approaches.

Attractive to funders because of its efficacy, computer-aided learning is studied to extend its subject flexibility and increase helpful feedback to teachers and students alike. 

Professor Heffernan

ARRS Project Assesses Student Understanding

Professor Beck leads the National Science Foundation-funded Automatic Reassessment and Retention System (ARRS) project, which evaluates student topic understanding. Rather than repeat the process of learning something only to quickly forget it, this research aims at periodically reassessing students on the topic. 

Earlier studies showed better knowledge retention with the latter approach, and current LST research furthers the creation of a system that will study how to optimize student learning through this method for better retention. 

Professor Beck

ASSISTments for Mathematics in Maine Schools

Funding of $3.5 million from the U.S. Department of Education underwrites this project in which LST students evaluate an online tutoring system's effectiveness for math homework.

In this study, the ASSISTments system will provide 7th-grade students at more than 50 schools in Maine with instant feedback and custom-tailored tutoring, while their teachers receive morning reports on students’ nightly progress, and instruction in how to adapt teaching plans to include this information. SRI International will externally evaluate student test results. 

Professor Heffernan