The Technology for Effective and Efficient Learning (TEEL) Lab at Carnegie Mellon University focuses on research in learning methods, technology for learning systems, curriculum development, and workforce training.

About TEELLab

 

The Technology for Effective and Efficient Learning (TEEL) Lab at Carnegie Mellon University focuses on research in learning methods, technology for learning systems, curriculum development, and workforce training at scale. The ultimate goal is to forge a path for lifelong learning and career ever-readiness through innovative bridging of learning between formal education, informal education, and the workplace.

The TEEL Lab develops learning platforms that support learning through discussion and collaboration, focusing on hands-on technology areas such as cloud computing, machine learning, AI, and data science. TEEL's interoperable online learning ecosystem is designed to enable effective and efficient learning through iterative and interactive learning coupled with contextualized and timely feedback, and by leveraging social interactions between students as a substantial learning resource.

The lab conducts research studies on student learning and evaluates innovative approaches for incorporating social and interactive learning as a driver for developing cognitive and meta-cognitive skills and motivation. Its work is based on research spanning more than a decade in Computer Science Education, Software Engineering, Learning Sciences, and Language Technologies.

In addition to scientific findings, technical advances, and principles for instructional design, the team’s past research has also produced open source resources to share with the broader community. Specific features of the LMS-pluggable online course infrastructure is that it enables project-based learning through auto-graded projects that provide contextualized feedback. We also incorporate social learning in the form of project reflection and feedback cycles after each project, timed and role-based online learning exercises and rubric-driven peer code-review.

Members of the TEEL Lab collaborate with other groups at CMU, other universities, community colleges as well as industry. Find out how you can get involved from our Call for Participation.

Funded in part by a grant from Microsoft and three NSF grants ACI-1443068, IIS 1822831 and IIS 1546393.

 

Foundational Research

 

    Technology Enhanced Learning

    Our research in Technology Enhanced Learning focuses on using technology to improve educational outcomes in computing education. This includes the incorporation of intelligent tutoring systems and online collaborative activities to scale learning for non-technical students, as well as the development of modular, auto-graded assignments that adapt to diverse learner needs. We also explore innovative methods like integrating conversational agents to foster reflection during collaborative programming.


    AI in Education

    Recent advances in AI have created new opportunities to interact with students at scale to improve learning; new temptations for students to abuse AI to complete their assignments; and new thinking about how job skills will shift in the near future as AI capabilities become ubiquitous. We have been investigating how computer science education's curriculum and methods must change to adapt.

    Empowering Instructors with Learning Analytics

    We are building data pipelines and dashboards to make students' achievements and difficulties in online work more visible to instructors, to allow personal intervention with students when they need it, and continual reflection and improvement of course curricula and platforms.


    Making Student Collaborations Effective

    Collaboration is a useful component of an online course because interaction can help with retention and motivation. It’s also been shown that people learn more by working with others. However not all interaction is equal, and the most fruitful interactions do not necessarily arise from any arbitrary opportunity for communication between students. Our research is aimed at understanding exactly what kinds of interactions are helpful and how to elicit interactions that have measurable impacts on things we care about such as learning, retention, self-efficacy, and belongingness. The research falls in two main categories: building lightweight automated agents that steer student conversations in ways that improve learning, and designing collaborative assignments that work well with these techniques.

    Fine-Grained Design of Group Tasks

    Research focusing on detailed aspects of designing and implementing group tasks in educational settings, including team formation strategies, collaborative programming activities, and the integration of intelligent agents to support group work. This work examines how specific design choices affect student interaction, learning outcomes, and team performance.

    Cloud Curriculum Development

    We are collaborating with other researchers to agree on industry standard sets of learning objectives for cloud computing courses. (2015-2020)

      Foster, D., White, L., Erdil, D. C., Adams, J., Argüelles, A., Hainey, B., … Stott, L. (2019) Toward a cloud computing learning community. Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, 143–155.

Faculty Team

Teaching Professor, Computer Science Department

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Majd Sakr

Associate Professor, Computer Science Department

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Seth Goldstein

Professor, Language Technologies Institute and Human-Computer Interaction Institute

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Carolyn Rosé

Systems Scientist, Institute for Software Research

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Chris Bogart

Research Associate, Computer Science Department

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Jaromir Savelka

Research & Curricula AI Technicians, Computer Science Department

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Can Kultur

Project Members

Lab Director

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Heather Burte

Data Scientist

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Adam Zhang

Engineering Lead

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Divya Prem

Full Stack Engineer

Halim-Antoine Boukaram

Full Stack Engineer

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Baljit Singh

Project Scientist

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Arav Agarwal

Frontend Developer

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Hanzhi Yin

UX Engineer

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Aparna Maya Warrier

Full Stack Developer

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Zhihan Zhang

Alumni

Project Scientist/p>

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Marshall An

PhD student, Language Technologies Institute

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Sreecharan Sankaranarayanan

Project Scientist

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Siddharth Kandimalla

Project Administrator

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Cait Frazier

Learning Engineer

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Michelle Fernandez


Resources

Sail()

Sail() is an online learning platform that provides college and university instructors with job-focused technology courses created at Carnegie Mellon University that are project-based, collaborative, and use real-time feedback. Sail() offers content, scaffolded activities, auto-graded projects with opportunities to iterate by providing timely feedback, social learning reflection and feedback cycles for each project, timed and role-based online group learning exercises, and rubric-driven peer code-review activities. The Sail() Platform enables evidence-based learning tools for instructors and researchers. Instructors can use the detailed dashboard to visually reflect on various aspects of student learning. Researchers can instrument and run various learning experiments to explore learning methods.

DiscourseDB

DiscourseDB is an NSF funded data infrastructure project designed to bridge data sources from multiple platforms for hosting those learning experiences. Our vision is to provide a common data model designed to accommodate data from diverse sources including but not limited to Chat, Threaded Discussions, Blogs, Twitter, Wikis, and Text messaging. We will make available analytics components related to constructs including role taking, help exchange, collaborative knowledge construction, showing openness, taking an authoritative stance, attitudes, confusion, alliance and opposition. In enabling application of such metrics across datasets from multiple platforms, research questions related to the mediating and moderating effect of these processes and state measures on information transfer, learning, and attrition can be conducted, building on the earlier research of our team.

Bazaar

Bazaar is a publicly available architecture for orchestrating conversational agent-based support for group learning. It is a powerful tool for facilitating research in collaborative learning. It hosts a library of reusable behavioral components that each trigger a simple form of support. More complex supportive interventions are constructed by orchestrating multiple simple behaviors. Its flexibility and simplicity mean it can be used to very rapidly develop platforms for investigating a wide range of important questions within the design space of dynamic support for collaborative learning.

LightSide

The open-source LightSide platform, including the machine-learning and feature-extraction core as well as the researcher's workbench UI, has been and continues to be funded in part through Carnegie Mellon University, in particular by grants from the National Science Foundation and the Office of Naval Research. We make the LightSide research platform freely available for research and education. In exchange, we ask that you provide us with basic information about who you are and how you're making use of LightSide's capabilities.

Social Recommendation

Massive Open Online Courses have experienced a recent boom in interest. Problems students struggle with in the discussion forums, such as difficulty in finding interesting discussion opportunities or attracting helpers to address posted problems, provide new opportunities for recommender systems. We developed a social recommendation technology to support help seekers in MOOC discussion forums implemented using a context-aware Matrix Factorization model to predict students' preferences for answering a given question. This recommendation framework allows for this two-way recommendation. .

Collaboration

 

The TEEL Lab at Carnegie Mellon University is excited to call for collaboration on the development and deployment of our SAIL Platform and Curriculum. We collaborate with faculty at community colleges to offer a variety of hands-on project-based courses as part of community college course offerings. We focus on curricula, developed by domain experts, that are targeted to the needs of a variety of learners to prepare them for jobs in the domains of cloud computing, machine learning, and data science, with the goal of helping meet the expected job growth in these fields. We prepare learners for these domains by developing foundational skills and providing opportunities to exercise industry’s best practices. This collaboration is meant to influence both the curricular content and its delivery while leveraging the expertise of faculty at community colleges in order to best meet the needs of our target learners in terms of career preparedness and the ability to cope with life-challenges that impact the learners’ ability to engage in a sustained upskilling effort. Mentorship and professional development in the domain is available to collaborating faculty.

Sail() platform offers content and scaffolded activities that integrate with Learning Management Systems that are already in use at community colleges. The design builds on an empirical foundation and software infrastructure emerging from our experience of offering online and hands-on project-based courses since 2013 to Carnegie Mellon's global campuses. Our platform offers learn-by-doing projects with scaffolding and opportunities to iterate by using auto-graded projects that provide timely and contextualized feedback. We incorporate social learning reflection and feedback cycles for each project, timed and role-based online group learning exercises and rubric-driven peer code-review opportunities. All of these are industry-standard job-relevant activities further reinforce the connection between the course content and software engineering best practices.

The TEEL Lab partners with community colleges to collaboratively adapt our existing curricular content to create new offerings or augment existing classes within the community college context. The SAIL Platform and collaboration is an effort to evaluate and enhance the efficacy of our innovative learning methods. We also are interested in research endeavors to investigate how to incorporate upskilling as part of a learner's current job.

Call to action: Reach out to sail-platform@andrew.cmu.edu

 

Positions

TEEL Lab Internships The TEEL Lab has several positions available for interns (in-person and remote). We have three types of roles we are looking to fill:
• Infrastructure development: writing and refactoring cloud apps, containerization, API development, data processing and analysis, front-end development
• Course development: writing content and auto-gradable assignments for courses related to cloud programming, Cloud DevOps, and data science/engineering
• Research: data mining and/or user centered design aimed at improving learning in online courses
Interested individuals should submit their CV here.

Get in touch

Email

sail-platform@andrew.cmu.edu