LS&T Affiliate Members
Learning Sciences & Technology Affiliate Membership
In addition to the students enrolled in the LS&T graduate programs, any member of the WPI community may apply to become an affiliate member.
Benefits of LS&T Affiliation:
Affiliated members are eligible to participate in LS&T colloquia and community events, which provide opportunities to obtain feedback about research from other LS&T researchers and to learn more about learning science. For researchers from other fields (e.g., computer science, math) who have interests in learning science, affiliate status may be useful when applying for LST-related jobs and grant opportunities.
How to Apply:
Any member of the WPI community (students, faculty, staff) can become an affiliate member of LS&T if (1) they are studying or researching topics that are substantively related to learning science and technologies, and (2) they are nominated by an LS&T faculty member. Nominees should submit a small portfolio of their work exhibiting interest in LS&T, e.g., a paper submitted to or published at an LST-related conference, a statement of purpose for a graduate school application that describes the person's LS&T interests, etc. The portfolio and a simple cover letter should be sent to the LST administrator, Angela Kao, under the subject heading "LST Affiliate Membership Query."
Anand Ramakrishnan
My name is Anand Ramakrishnan, and I am a doctoral candidate in the computer science department at Worcester Polytechnic Institute (WPI) working with Professor Jacob R. Whitehill in the field of machine learning. We are currently building an end-to-end deep learning system to analyze teaching videos and predict the quality of student-teacher interactions in them. To this end, we are currently collaborating with Professor Lane Harrison, Professor Erin Ottmar from SSPS WPI, and Professor Jennifer LoCasale-Crouch from the University of Virginia. I am also currently collaborating with Dr. Warren Jackson from PARC, where I am working on understanding hybrid model compositions for better system modeling. My website: https://www.anandramakrishnan.me/ | A recent paper: https://arxiv.org/pdf/2005.09525.pdf
Han Jiang
I am a Ph.D. candidate in Data Science working with Dr. Jacob Whitehill. My research is focused on the applications of machine learning and educational data mining. One project explores the relationships among thermal comfort, engagement, and learning in a lab-based experiment video dataset. For example, we build end-to-end models to measure students’ thermal comfort and engagement from students’ faces. Our most recent work is to investigate whether a tutor's emphatic message in practice sessions via iPad in a lab-based experiment can influence the students’ emotions and heart rate, and if so, whether we can measure the influence from webcam and heart monitor sensors. Finally, we are also working on a project to investigate whether CNNs consider the semantic features (e.g., correctness of math equations) as one kind of bias when we are detecting math equations from images.