Learning Sciences & Technologies

Faculty

Learning Sciences & Technologies

Core Faculty

Neil T. Heffernan, Associate Professor and Executive Director; Ph.D., Carnegie Mellon University; Intelligent tutoring agents, artificial intelligence, cognitive modeling, machine learning

Janice Gobert, Associate Professor and Director; Ph.D., University of Toronto; learning with visualizations and simulations in science; learning and assessment with technology; learner characteristics and their relationship to content learning

Ryan S.J.d. Baker, Assistant Professor; Ph.D., Carnegie Mellon University; educational data mining, learner-computer interaction, gaming the system, student modeling, intelligent tutoring systems, educational games

Joseph E. Beck, Assistant Professor; Ph.D., University of Massachusetts, Amherst; educational data mining, student modeling, Bayesian Networks, student individual differences

Learning Sciences & Technologies

Associated Faculty

David C. Brown, Professor; Ph.D., Ohio State University; Knowledge-based design systems, artificial intelligence

James K. Doyle, Associate Professor; Ph.D., University of Colorado/Boulder; judgement and decision making, mental models of dynamic systems, evaluation of interventions

Kathi Fisler, Associate Professor; Ph.D., Indiana University; Interplay of human reasoning and formal logic in the context of hardware and software systems; current projects explore access-control policies and diagrams.

George T. Heineman, Associate Professor; Ph.D., Columbia University; Component- based software engineering, formal approaches to compositional design

Arthur C. Heinricher, Professor; Ph.D., Carnegie Mellon University; applied probability, stochastic processes and optimal control theory

Robert W. Lindeman, Associate Professor; Ph.D., George Washington University; Human- computer interaction, haptics, virtual environments

Charles Rich, Professor; Ph.D., Massachusetts Institute of Technology; Artificial intelligence and its intersections with human-computer interaction, interactive media and game development, robotics, intelligent tutoring systems, knowledge-based software tools

Carolina Ruiz, Associate Professor; Ph.D., University of Maryland; Data mining, knowledge discovery in databases, machine learning

Jeanine L. Skorinko, Assistant Professor; Ph.D., University of Virginia; social environmental cues, stigmas and stereotyping, perceptions of others

Program of Study

The Learning Sciences and Technologies (LS&T) program offers graduate studies toward the MS and PhD degrees. Our state-of-the-art facilities, faculty and strong relationships with K-12 schools provide students with the resources to perform innovative scientific research at the highest level. The diverse learning environment that characterizes our program promotes easy exchange of ideas, access to all the necessary resources, and encourages creative solutions to pressing scientific questions. The LS&T program is based on three affiliated areas – Computer Science, Cognitive and Educational Psychology, and Statistics – and provides opportunities for advanced course work and research for highly qualified students.

Admissions Requirements

Applicants must apply directly to the LS&T program. In order to be capable of performing graduate level work, applicants should have background in at least one of the core disciplines of LS&T, namely, Cognitive/Educational Psychology, Computer Science, or Statistics. We will also consider applicants whose academic background is in Science or Math.

A student may apply to the PhD program in LS&T after completing a bachelor’s degree (in which case a master’s degree must first be completed) or a master’s degree in one of the affiliated areas (Computer Science, Cognitive or Educational Psychology or Statistics) or a closely related area. Applicants with other degrees are welcome to apply if they can demonstrate their readiness through other means, such as GRE Subject exams in an affiliated area, or through academic or professional experience. GRE scores are strongly recommended, but not required, for all applicants. Inquiries about the GRE should be made to Dr. Neil Heffernan or Dr. Janice Gobert.

Degree Requirements

For the M.S.

The student may choose between two options to obtain the M.S. degree: thesis or coursework. Students should carefully weigh the pros and cons of these alternatives in consultation with their LS&T faculty advisor prior to selecting an option. Completion of the M.S. degree requires 33 graduate credit hours. M.S. LS&T students who wish to become doctoral candidates in LS&T must first complete their M.S. degree in LS&T following the thesis option.

To satisfy the interdisciplinary nature of the LS&T program, each M.S. student must complete the following 15 graduate credit hours that form the core requirements.

  • Computer Science Requirement [6 graduate credit hours]
    Two LS&T Computer Science courses
  • Cognitive Psychology Requirement [6 graduate credit hours]
    Two LS&T Cognitive Psychology courses
  • Statistics Requirement [3 graduate credit hours]
    One LS&T Statistics course; or CS 567. Empirical Methods for Human-Centered Computing

No single graduate course can be double counted to satisfy two of the above requirements.

MS in LS&T – Coursework Option

In addition to the 15 graduate credit hours as required by the M.S. core requirements, a student pursuing the coursework option must register for an additional six graduate courses (totaling 18 graduate credit hours). To ensure a sufficient focus on LS&T, two of these courses (for a total of 6 graduate credit hours) must be from the LS&T course list. The remaining four courses (for a total of 12 graduate credit hours) are electives that relate to the student’s individual program of study and must be selected in consultation with the student’s LS&T advisor.

Note that MS graduate credits cannot be from independent study/research courses except by approval of the LS&T Program Director.

MS in LS&T – Thesis Option

In addition to the 15 graduate credit hours as required by the M.S. core requirements, a student pursuing the thesis option must satisfactorily complete a written thesis. Any Core or Associated LS&T faculty may serve as the thesis advisor. A thesis consisting of a research or development project worth a minimum of 9 graduate credit hours must be completed and presented to the LS&T faculty. A thesis proposal must be approved by the Core LS&T faculty and the student’s advisor before the student can register for more than four thesis credits.

To complete the remaining 9 graduate credit hours, the student must register for an additional three graduate courses. To ensure a sufficient focus on LS&T, two of these courses (for a total of 6 graduate credit hours) must be from the LS&T course list. The remaining course (of 3 graduate credit hours) is an elective that relates to the student’s individual program of study and must be selected in consultation with the student’s LS&T advisor. As for the coursework option, M.S. graduate credits cannot be from independent study/ research courses except by approval of the LS&T Program Director.

No Combined BS/M.S. Degree

The LS&T program does not offer a combined B.S./M.S. degree.

For the Ph.D.

Students are advised to contact the program director for detailed program guidelines, in addition to the university’s requirements for the Ph.D. degree. Students who wish to pursue a Ph.D. in LS&T who completed their M.S. at WPI in LS&T, must have chosen the thesis option.

Fundamentally, it is expected that all LS&T Ph.D. students master the basics of Learning Sciences, apply those concepts to create an innovative technology, and properly analyze their work with the appropriate statistical techniques. Ph.D. students will receive training through a combination of enrolling in courses, satisfying competency requirements and completing a dissertation; all Ph.D. students will be reviewed by the Core LS&T faculty at least once a year to see that they are making satisfactory progress towards these three components of the Ph.D. program.

Course Requirements

The Ph.D. degree in LS&T requires an additional 60 graduate credit hours of work beyond the M.S. degree. Students must take a minimum of 30 graduate credit hours of course work, including independent study, and 30 graduate credit hours of research.

To satisfy the interdisciplinary nature of the LS&T program, each Ph.D. student must complete the following 24 graduate credit hours. To count towards the course requirements, students must get a minimum grade of B for each of the courses. Students receiving a C or below must retake a course in the appropriate area and receive a B or higher.

  • Computer Science Requirement [9 graduate credit hours]
    Three LS&T Computer Science courses
  • Cognitive Psychology Requirement [9 graduate credit hours]
    Three LS&T Cognitive Psychology courses
  • Statistics Requirement [6 graduate credit hours]
    LS&T Statistics courses, or CS 567. Empirical Methods for Human-Centered Computing

All students are required to submit a program of study that describes their planned course work; their LS&T advisor must approve the program. These classes can include graduate classes at WPI, classes at Clark University, particularly from their Psychology Department, and from independent studies. However, to ensure depth in LS&T, no more than 9 credit hours can be from disciplines other than Cognitive Psychology, Computer Science, and Statistics except by the approval of the Program Director.

Students can count previously taken LS&T courses towards these requirements. However, students must still complete 30 graduate credit hours of coursework for the Ph.D. degree. For example, if a student had taken two LS&T Computer Science courses as part of an LS&T M.S. degree, only one more LS&T Computer Science course would be required, but the student would still have to complete 30 graduate credit hours of coursework for the Ph.D. Similarly, students who are transferring in with an MS degree will be evaluated for which requirements they have fulfilled, but will still be required to take 30 graduate credit hours of coursework.

To complete the remaining 6 graduate credit hours, the Ph.D. student can register for other graduate courses or independent studies with approval of the student’s LS&T advisor.

Competency Requirements

In addition to successful completion of their coursework, Ph.D. students must demonstrate competency in two core areas: Data Analysis and Communication (specifically, Speaking and Writing). Regarding Data Analysis, it is expected that students will learn analysis methods relevant to the Learning Sciences. We have selected these two areas as they are fundamental to success as an empirical scientist and will form the basis of LS&T graduates’ future careers.

Competency in both Data Analysis and Communication will be assessed as follows: Students will be expected to conduct a pilot research study towards their graduate research. Students will submit a short paper (10-20 pages) to the Core LS&T faculty who will write a set of questions to be asked during a public presentation by the graduate student of the pilot research project. Possible venues for this include the AIRG (Artificial Intelligence Research Group) or the Learning Sciences Seminar. Students will be graded by at least two Core LS&T faculty on their responses to the LS&T questions, their data analysis, and communication skills at handling spontaneous questions during the talk. This requirement will be handled by the Core LS&T faculty.

Students must complete this competency requirement prior to defending their Ph.D. proposal. Furthermore, competency requirements must be completed within four semesters after students begin as Ph.D. students, except by permission of the Program Director.

Dissertation Requirements

Within six semesters of being admitted to the LS&T Ph.D. program, each student must form a dissertation committee, and write and defend a dissertation proposal. Any deviation from the timetable for the dissertation must be approved by the Program Director. Any Core or Associated LS&T faculty may serve as a research advisor.

A student’s dissertation committee is composed of at least four members, as approved by the LS&T Core faculty. The committee must contain at least one Core LS&T faculty member and one faculty member external to WPI. To reinforce the interdisciplinary nature of the degree, at least two of the three cooperating departments (Computer Science, Social Science and Policy Studies and Mathematical Sciences) must have a faculty member on the dissertation committee. The dissertation committee will be responsible for approving the dissertation proposal and final report.

Students must enroll in at least 30 credits for their dissertation. Before presenting and defending their dissertation proposal, students may only enroll in 15 graduate research credit hours. Students are expected to defend their dissertation within six semesters of the acceptance of their dissertation proposal. In addition to the minimum of 30 graduate credit hours of research, the dissertation culminates in the student submitting the document itself and a public defense of the research.

Courses

LS&T Computer Science Courses

  • CS 509 Design of Software Systems
  • CS 534 Artificial Intelligence
  • CS 538 Knowledge Based Systems
  • CS 539 Machine Learning
  • CS 540 Artificial Intelligence in Design
  • CS 546 Human-Computer Interaction
  • CS 548 Knowledge Discovery and Data Mining
  • CS 565 User Modeling
  • CS 566 Graphical Models for Reasoning Under Uncertainty
  • CS 567 Empirical Methods for Human-Centered Computing
  • CS 568 Artificial Intelligence for Adaptive Educational Technology

LS&T Cognitive Psychology Courses

  • PSY 501 Foundations of the Learning Sciences
  • PSY 502 Learning Environments in Education
  • PSY 503 Research Methods for the Learning Sciences
  • PSY 504 Meta-cognition, Motivation, and Affect
  • PSY 505 Advanced Methods and Analysis for the Learning and Social Sciences

LS&T Statistics Courses

  • MA 511 Applied Statistics for Engineers and Scientists
  • MA 540/4631 Probability and Mathematical Statistics I
  • MA 541/4632 Probability and Mathematical Statistics II
  • MA 542 Regression Analysis
  • MA 546 Design and Analysis of Experiments
  • MA 547 Design and Analysis of Observational and Sampling Studies
  • MA 554 Applied Multivariate Analysis
  • MA 556 Applied Bayesian Statistics
 
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