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The BS/MS program in Learning Sciences & Technologies allows students to pursue a five-year Bachelor's/Master's program, in which the Bachelor's degree is awarded in any related major offered at WPI and the Masters degree is awarded in Learning Sciences & Technologies.
Program Description
Students enrolled in the B.S./M.S. program must satisfy all the program requirements of their respective B.S. degree and all the program requirements of the M.S. degree in Learning Sciences & Technologies. WPI allows B.S./M.S. students to double-count courses towards both their undergraduate and graduate degrees whose credit hours total no more than 40 percent of the 33 graduate credit hours required for the M.S. degree in Learning Sciences & Technologies (i.e., up to 13 graduate credits or equivalently 2 undergraduate units), and that meet all other requirements for each degree. These courses can include graduate courses as well as certain undergraduate 4000-level courses, listed below, that are acceptable for satisfying Learning Sciences & Technologies M.S. requirements. The degree is open to new entrants in either fall or spring semesters and can be completed either full time or part time. This degree is not available online.
Admission Requirements
Timing: Students are expected to apply for admission to the B.S./M.S. program during their junior year so that they have sufficient time to plan their course selection with their major Academic Advisor and the Learning Sciences & Technologies Program Director.
Prior to submitting their application, prospective students should do the following:
Update their course trackers, see for example one of the following: Computer Science, Psychology, Mathematical Sciences, and
Arrange a meeting with one of the core LS&T faculty members: Profs. Neil Heffernan (CS), Erin Ottmar (Psychology), Adam Sales (Math), Stacy Shaw (Psychology), or Jacob Whitehill (CS).
During your meeting with LS&T core faculty, you can expect to discuss your suitability for the program, to determine which courses you may double-count toward your degree, and to draft a plan of study (form TBD).
Application requirements:
(Required) Two letters of recommendation, one of which comes from a core LS&T faculty member.
(Required) A statement of purpose, specifying whether you will pursue a thesis or coursework track for your masters, your goals for your master's degree, and your main topics of interest.
(Required) Transcript
(Optional Items) A resume, GRE scores, supplemental writing samples.
Double Counting Credits: Once you determine which courses can be double-counted, you will need the LS&T Program Director, Neil Heffernan, to sign this form.
Double Counting Courses
4000-level courses and projects that can be double-counted: For the 4000-level courses listed below, two graduate credits will be earned towards the B.S./M.S. degree if the student achieves a grade of B or higher.
Computer Science courses:
CS 4341. Introduction to Artificial Intelligence
CS 4342. Machine Learning
CS 4432. Database Systems II
CS 4445. Data Mining and Knowledge Discovery in Databases
CS 4518. Mobile and Ubiquitous Computing
Data Science courses:
DS 4635/MA 4635. Data Analytics and Statistical Learning
DS 4433 Big Data Management and Analytics
Mathematics courses:
MA 4631. Probability and Mathematical Statistics I
MA 4632. Probability and Mathematical Statistics II
MA 4635/DS 4635. Data Analytics and Statistical Learning
Psychological Science courses:
PSY 4800. Special Topics in Psychological Science
PSY 4900. Advanced Research in Psychological Science
Business courses:
MIS 4741 User Experience and Design (Business)
Other Business Courses could be double counters where the project work has a substantial overlap with Learning Science issues (like a final project that relates to student learning issues). Students who are interested in being able to double count a class are encouraged to petition the LS&T director explaining how their project work relates Some such classes that might qualify...
MIS 4720. Systems Analysis and Design
MIS 4084. Business Intelligence
Neuroscience Courses:
Currently Neuroscience does not have a 4000 class listed but we imagine that over time such classes could be approved so we list Neuroscience as one of the departments whose classes could count toward this.
Major Qualifying Project (MQP):
Up to 3 graduate credits (equal to 1/2 undergraduate unit) can be earned towards fulfillment of the Learning Sciences & Technologies thesis requirement by double counting a Major Qualifying Project, provided that:
the MQP involves substantial use of Learning Sciences & Technologies at an advanced level;
the thesis research is a continuation or extension of the MQP work;
the student satisfies the thesis requirement by completing at least 6 additional credits of PSY 599 Thesis Research, and the M.S. thesis advisor and the Learning Sciences & Technologies Faculty Steering Committee approve the double-counting.
MQP work may not be double-counted toward the non-thesis option.
Other 4000-level courses and independent studies not on this list but relevant to Learning Sciences & Technologies M.S. requirements may be petitioned to double-count. Such petitions need to be approved by the Learning Sciences & Technologies Faculty Program Director.
Graduate courses that can be double-counted:
A student in the B.S./M.S. Program in Learning Sciences & Technologies can double-count any of the graduate courses that are listed in the Learning Sciences & Technologies WPI Graduate Catalog. Special topics courses or independent study classes need to be approved by the LST Program Director before they can be used for double counting.
Restricted Undergraduate and Graduate Course Pairs
Some undergraduate and graduate courses have significant overlap in their content. The following table lists these courses. A student can receive credit towards their M.S. degree for at most one of the two courses in any row of this table.
Courses in Computer Science
Undergraduate Course Graduate Course
CS 4341 Introduction to Artificial Intelligence CS 534 Artificial Intelligence
CS 4342 Machine Learning CS 539 Machine Learning
CS 4432 Database Systems II CS 542 Database Management Systems
CS 4445 Data Mining and Knowledge Discovery in Databases CS 548 Knowledge Discovery and Data Mining
CS 4518 Mobile and Ubiquitous Computing CS 528 Mobile and Ubiquitous Computing
Courses in Mathematics
Undergraduate Course Graduate Course
MA 4631 Probability and Mathematical Statistics I MA 540 Probability and Mathematical Statistics I
MA 4632 Probability and Mathematical Statistics II MA 541 Probability and Mathematical Statistics II
DS 4635/MA 4635 Data Analytics and Statistical Learning MA 543/DS 502 Statistical Methods for Data Science
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