Our Research
In many professional fields, the workplace is a rapidly changing environment. Our research equips professional educators and learning managers with the tools they need to accommodate learning designed for today’s changing workplace ecosystems.
We have seen first-hand how advances in online and blended learning have impacted the lives and careers of thousands of learners as well as influenced how we teach on campus at MIT. While our field of teaching expertise has been in Supply Chain Management, we foresee that the insights, technological considerations, and pedagogical innovations discovered can apply to teaching and learning across disciplines. Our research is relevant to both the academy and to industry.
Research Areas
Omnichannel Education Explores Hybrid Formats
How might we better engage learners through optimal combinations of omnichannel learning? What are the learning and engagement benefits and challenges resulting from various combinations of synchronous vs. asynchronous and online vs. in-person teaching methods? How are methods best combined? These are some of the questions we answer with this research.
From pure online and asynchronous, to pure in-person and synchronous formats, including all the mixed hybrid models in-between, we explore:
(i) which format is more appropriate for what type of content,
(ii) which teaching methodology is more appropriate for each format,
(iii) who is the most appropriate target audience for each format.
Engagement and Dropout
How might we increase engagement in massive open online courses (MOOCs) and in MOOC-based programs?
We develop machine-learning models to better predict students at-risk of dropping out. We design and evaluate the effectiveness of strategies and interventions to increase engagement in a massive, open, and online learning environments.
Community Building
How might we build a community of learners in a virtual, massive, and open learning environment?
How might we identify the best strategy and channel to:
- encourage new members to get involved in the online community and create social engagement
- focus learning and connect learners in specific cohorts
- create exclusivity, only for a closed group of program completers
Manage & Track
How might we improve the courses and their programs based on big data?
We explore the role of data analytics in monitoring and evaluating the performance of courses and online programs and in enabling data-driven decision making to guide program and course improvements.
Learning & Assessment
This research area aims to investigate the fundamental trade-offs involved with providing an educational platform that accommodates both learning and assessment within the same platform. The research proposes strategies to ensure academic integrity in a MOOC-based program.
Research Projects
Meta-LAD: a dashboard supporting metacognition
Meta-LAD: a dashboard supporting metacognitionThe project aims to support online learners’ self-regulated learning, performance, and retention by designing a learning analytics dashboard (LAD). The research questions: What are indicators predicting learners’...
Converting zombies into learners
The project aims at increasing learners’ engagement in massive, open, and online education in Supply Chain Management by applying learning analytics.
Learning & Assessment
In this project we investigate the fundamental trade-off involved with providing an educational platform for both learning and assessment, and propose strategies to ensure academic integrity in a MOOC-based program
A data-driven framework to evaluate design changes in MOOCs
This research aims to develop a data-driven framework to evaluate design changes in MOOCs. We explore a change from multiple angles-process, proficiency, and perception- and apply various analytical methods-temporal, causal and predictive to map out the outcome of instruction along multiple dimensions of learning.
Predicting the likelihood of dropping out from MOOC-based program
This research aims to identify the likelihood of dropping out from a MOOC-based program. Program dropout happens both, within and between courses. We identify the key dropout factors and develop a machine learning model to predict future student dropout.