Marcelo Worsley
Faculty Profile
The Karr Family Associate Professor in Computer Science and Learning Sciences
Associate Professor, Learning Sciences
Biography
I am a dual American and Brazilian citizen. I was born in Brazil and moved to the United States at 3 years old. When I was 9 years old my family moved to Belgium where I completed the majority of my primary education. Upon completing middle school, my family returned to Michigan, where I finished high school. In 2003, after high school, I moved to California to study Chemical Engineering and Portuguese at Stanford University, where I later completed a master’s degree in Computer Science and a PhD in Learning Sciences and Technology Design.
When I’m not doing research I can normally be found cooking, reading, gardening, spending time with my family, or participating in a range of sporting activities: skiing, running, basketball, biking, capoeira, gymnastics, swimming, and soccer. I also like to take advantage of any opportunity to try or build something new. In the kitchen, this amounts to coming up with new recipes. In other areas, it results in a lot of “protoypes” that my oldest daughter gets to inherit as toys and props for fantasy play and drawing.
Websites
Education
| Year | Degree | Institution |
| 2014 | PhD, Learning Sciences and Technology Design | Stanford University |
| 2014 | MS, Computer Science | Stanford University |
| 2007 | BA, Spanish and Portuguese | Stanford University |
| 2007 | BS, Chemical Engineering | Stanford University |
Research
I have carried many titles in recent history: Education Research, Computer Scientist, Chemical Engineer, Social Entrepreneur, Humanist, Teacher, Medical Researcher, Mentor, Tutor, Volunteer, Technology Consultant. Throughout these titles, I have maintained a central focus on promoting and improving Science, Technology, Engineering and Mathematics (STEM) education among underserved populations through hands-on learning. My objective is not that all students necessarily pursue STEM careers. Instead, I want for students to realize their agency as innovators and inventors and use that lens to critically examine the world around them. To this end, my goal is for students to be able to create solutions to the problems that they encounter, regardless of the context, by drawing on their unique set of life experiences and perspectives. At the same time I want to empower students to have access to the resources that they need in order to bring their ideas to fruition. This is one of the reasons for my current research efforts in multimodal learning analytics. To support these efforts, my past and present research has been dedicated to doing process-oriented studies and can be divided into four primary areas: designing meaningful hands-on learning experiences; extracting multimodal data; analyzing and interpreting multimodal data; developing naturalistic interfaces.
Selected Publications
Works in Progress
Worsley, M. & Blikstein, P. (under review). On the Origins of Students’ Ideas: Identifying Reasoning Strategies in the Context of Engineering Design with Everyday Materials. The Journal of Pre-College Engineering Education Research.
Worsley, M. & Blikstein, P. (under review). A Multimodal Analysis of Making. International Journal of Artificial Intelligence in Education.
Dissertation
Worsley, M. (2014). Making with Understanding: Research on Studies from a Constructionist Learning Environment. PhD Dissertation Stanford University. [pdf]
Papers
Ochoa, X. & Worsley, M. (2016). Augmenting Learning Analytics with Multimodal Sensory Data. Journal of Learning Analytics, 3(2), 213–219. http://dx.doi.org/10.18608/jla.2016.32.10 [pdf]
Worsley, M., Abrahamson, D., Blikstein, P., Grover, S., Schneider, B., & Tissenbaum, M. (2016). Situating multimodal learning analytics. In C.-K. Looi, J. L. Polman, U. Cress, & P. Reimann (Eds.), “Transforming learning, empowering learners,” Proceedings of the International Conference of the Learning Sciences (ICLS 2016) (Vol. 2, pp. 1346-1349). Singapore: International Society of the Learning Sciences. [pdf]
Worsley, M. Scherer, S., Morency, L.P., & Blikstein, P. (2015). Exploring Behavior Representation for Learning Analytics. In Proceedings of the 2015 International Conference on Multimodal Interaction. ACM, New York, USA. pp. 251-258. [pdf]
Worsley, M. & Blikstein, P. (2015). Using Learning Analytics to Study Cognitive Disequilibrium in a Complex Learning Environments. In Proceedings of the 5th Annual Conference on Learning Analytics and Knowledge, ACM, New York, USA. pp. 426-247. [acm]
Ochoa, X., Worsley, M., Chiluiza, K., & Luz, S. (2014). MLA’14: Third Multimodal Learning Analytics Workshop and Grand Challenges. In Proceedings of the 16th International Conference on Multimodal Interaction (ICMI ’14). ACM, New York, NY, USA. pp. 531-532. [acm]
Worsley, M. (2014). Multimodal Learning Analytics as a Tool for Bridging Learning Theory and Complex Learning Behaviors. In Proceedings of the 2014 ACM workshop on Multimodal Learning Analytics Workshop and Grand Challenge (MLA ’14). ACM, New York, NY, USA. pp. 1-4.[pdf]
Worsley, M. & Blikstein, P. (2014). Deciphering the Practices and Affordances of Different Reasoning Strategies through Multimodal Learning Analytics. In Proceedings of the 2014 ACM workshop on Multimodal Learning Analytics Workshop and Grand Challenge (MLA ’14). ACM, New York, NY, USA. pp. 21-27. [pdf]
Blikstein, P., Worsley, M., Piech, C., Gibbons, A., Sahami, M., & Cooper, S. (2014). Programming Pluralism: Using Learning Analytics to Detect Patterns in Novices’ Learning of Computer Programming. International Journal of the Learning Sciences. Vol. 23, Iss. 4. pp. 561-599. [pdf]
Worsley, M. & Blikstein, P. (2014). The Impact of Principle-Based Reasoning on Hands-on, Project-Based Learning. In Proceedings of the 2014 International Conference of the Learning Sciences. Vol 3. pp 1147-1151. [pdf]
Worsley, M. & Blikstein, P. (2014). Using Multimodal Learning Analytics to Study Learning Mechanisms. In Proceedings of the 2014 Educational Data Mining Conference. pp. pp 431-432. [pdf]
Worsley, M. & Blikstein, P. (2014). Making Smarter not Harder: Using Principle-based Reasoning to Promote Object Closeness and Improve Making. In Proceedings of the 2014 FabLearn Conference.[pdf]
Worsley, M. & Blikstein, P. (2014). Analyzing Engineering Design through the Lens of Computation. Journal of Learning Analytics.[pdf]
Morency, L. Oviatt, S., Scherer, S. Weibel, N. & Worsley, M. (2013). ICMI 2013 Grand Challenge Workshop on Multimodal Learning Analytics. In Proceedings of the 15th ACM international conference on Multimodal interaction (ICMI ’13). ACM, New York, NY, USA. [pdf]
Worsley, M., &; Blikstein, P. (2013). Programming Pathways: A Technique for Analyzing Novice Programmers’ Learning Trajectories. In Artificial Intelligence in Education. Springer Berlin Heidelberg. 844-847. [pdf]
Gomes, J. Yassine, M., Worsley, M., & Blikstein, P. (2013) Analysing Engineering Expertise of High School Students Using Eye Tracking and Multimodal Learning Analytics. In Proceedings of the Educational Data Mining 2013 (EDM ’13). Memphis, TN, USA. 375-377. [pdf]
Worsley, M. (2012). Multimodal Learning Analytics: Enabling the Future of Learning through Multimodal Data Analysis and Interfaces. In Proceedings of the 14th ACM international conference on Multimodal interaction (ICMI ’12). ACM, New York, NY, USA. 353-356. Doctoral Consortium Best Paper Runner-up. [pdf]
Scherer,S., Worsley,M. & Morency, L. (2012). 1st international workshop on multimodal learning analytics: extended abstract. In Proceedings of the 14th ACM international conference on Multimodal interaction (ICMI ’12). ACM, New York, NY, USA, 609-610. [pdf]
Worsley, M. & Blikstein, P. (2012). An Eye For Detail: Techniques For Using Eye Tracker Data to Explore Learning in Computer-Mediated Environments. In the Proceedings of the 2012 International Conference of the Learning Sciences (ICLS ’12). Sydney, Australia. 561-562. [pdf]
Worsley, M., Johnston, M. & Blikstein P. (2011). OpenGesture: a low cost authoring framework for gesture and speech based application development and learning analytics. In Proceedings of the 10th International Conference on Interaction Design and Children (IDC ’11). ACM, New York, NY, USA. 254-256. [pdf]
Worsley, M. & Johnston, M. (2010). Multimodal Interactive Spaces: MagicTV and MagicMAP. Spoken Language Technology Workshop (SLT ’10), IEEE. San Francisco, CA, USA. 161-162. [pdf]
Conference Presentations
Worsley, M. & Blikstein, P. (2014). An Approach for Combining Qualitative Analysis with Learning Analytics to Study Learning Processes in Open-Ended Environments. Paper Presented at the 2014 American Education Research Association (AERA) Annual Conference.
Worsley, M. & Blikstein, P. (2013). Designing for Diversely Motivated Learners. Paper Presented at the Digital Fabrication and Making In Education Workshop at the 2013 Interactive Design for Children Conference (IDC 2013), New York, NY, USA. [pdf]
Worsley, M. & Blikstein P. (2012). A Framework for Characterizing Student Changes in Student Identity During Constructionist Learning Activities. Paper Presented at Constructionism 2012. Athens, Greece. [pdf]
Worsley, M. & Blikstein P. (2012). OpenGesture: A Low-Cost, Easy-to-Author Application Framework for Collaborative, Gesture-, and Speech-Based Learning Applications. Paper Presented at the Annual Meeting of the American Education Research Association (AERA). Vancouver, Canada. [pdf]
Blikstein, P., Safdari. M., & Worsley, M. (2012) Using Dynamic Time Warping and Cluster Analysis to Analyze the Learning of Computer Programming. Paper Presented at the Annual Meeting of the American Education Research Association (AERA). Vancouver, Canada.
Blikstein, P., Safdari. M., & Worsley, M. (2012) Using Dynamic Time Warping and Cluster Analysis to Analyze the Learning of Computer Programming. Paper Presented at the 10th Annual International Conference of the Learning Sciences (ICLS). Sydney, Australia.
Projects
Designing Meaningful Hands-on Learning Experiences
Students as Change Agents
One of the exciting aspects of student-centered learning is the realization that students can be change agents. A central element of my work involves helping students realize their agency, and acknowledge that they have a wealth of ideas that can be useful for solving both local, national and internationl problems. This is done through a combination of open-ended and thematic projects that expose them to some of the inner-workings of digital technology.
Teachers as Makers
Effecting change in schools is often times mediated through teachers, the trained individuals who interact with students on a daily basis. While teachers are tasked with bringing out innovation and creativity in their students, teachers should also see themselves as innovative and creative individuals. Hence, a portion of my work both domestically and internationally is aimed towards training teachers to not only be capable at using digital fabrication and invention, but also helping teachers see their own creativity. Furthermore, in addition to fostering a sense of innovation among teachers, this work also involves helping teachers develop curricular units that truly embody the ideas of constructionism.
Extracting Multimodal Data
Semi-Supervised Object Tracking
There are a range of interesting materials available for students to use when building and inventing. Moreover, their building process highlights their level of cognition and engineering knowledge. This project uses a collection of computer vision techniques in order to capture and track the movement of objects without the need for instrumentation.
Low-Cost Student Localization and Collaboration
Student collaboration and use of external resources play a central part in many learning environments. This project aims to provide a low-cost solution for storing this information by using wireless signals in conjunction with Machine learning and Probabilistic Modeling in order to approximately localize students and detect student collaboration.
Multimodal Data Capture for Situated Settings
A primary challenge of multimodal learning analytics is collecting data that is of sufficiently high quality for computational analysis, while also being collected with the goal of doing rich analyses. This particular projects aims to bridge the learning sciences and computer science community by developing guidelines, and sample hardware/software tools that can be used to capture and analyze multimodal data using cutting edge artificial intelligence, and in the support of constructs that are relevant to education researchers. This work is currently funded by a NSF EAGER.
Analyzing and Interpreting Multimodal Data
Building as Assessments
Building Assessments studies ways that expertise is evidenced in the microscopic and macroscopic actions that students take when building physical artifacts. A combination of building actions, student gesture data and spoken language can be used to identify student competencies and opportunities for additional learning.
Programming Pathways
Students approach computer programming with different intuitions and approaches. Often times the approaches utilized suggest underlying cognitive processes, and misconceptions. This project analyzes student programming pathways by using novel machine learning algorithms in order to exam common pathways that students follow both within a given assignment and across assignments. This project also studies qualitative learning data about each student as well as they help seeking behavior.
Sentiment, Speech and Drawing Analysis
This work focuses on the intersection of speech, sentiment and drawing for studying short-term and long-term engineering proficiency in constructionist learning environments.
Identity in Construction
Identity formation is a primary area of development in constructionist and project-based learning environments. This project studies how these changes are manifested through students actions and speech.
Studying Practices of Effective Learning Strategies
One affordance of multimodal analysis is the ability to capture and compare the multimodal behaviors associated with different learning/ teaching strategies. This work does just that by identifying the underlying behaviors that differentiate different teaching/learning strategies.
Deciphering Complex Instruction via Multimodal Learning Analytics
Complex instruction includes opportunities for mixed ability groups to engage in co-construction of knowledge through in-depth discussions and group collaboration. However, in the context of complex instruction, a variety of socio-emotional behaviors are certain to emerge. In order to validate and better support these behaviors, we are leveraging multimodal learning analytics, with a particular focus on the discourse, sentiments and non-verbals cues that are exhibited across repeated group interactions.
Developing Naturalistic Interfaces
OpenGesture
OpenGesture is an open-source, low-cost, easy to author, application framework for collaborative, gesture and speech based learning applications. The platform is designed to enable students, teachers, researchers, practitioners and parents to author innovative learning applications that are built on the cutting edge of human computer interface technology. Furthermore, the platform supports extensive opportunities to collect data about how users are engaging with the various applications.
MAGIC TV and MAGIC MAP are two applications that enables the user to use speech and gestures to interact with their television. These applications leverage Nintendo Wiimotes, microphone arrays and proprietary speech recognition technology to create an intuitive and easy to use environment for the average user. One particular feature of interest is the system’s ability to automatically differentiate between system directed speech and non-system directed speech (i.e. speech that is intended for another person).