FFILE 2013 - Workshop on Formative Feedback in Interactive Learning Environments

Educators and researchers have long recognized the importance of formative feedback for learning. Formative feedback helps learners understand where they are in a learning process, what the goal is, and how to reach that goal. While experimental and observational research has illuminated many aspects of feedback, modern interactive learning environments provide new tools to understand feedback and its relation to various learning outcomes.
Specifically, as learners use tutoring systems, educational games, simulations, and other interactive learning environments, these systems store extensive data that record the learner’s usage traces. The data can be modeled, mined and analyzed to address questions including when is feedback effective, what kinds of feedback are effective, and whether there are individual differences in seeking and using feedback. Such an empirical approach can be valuable on its own, and it may be especially powerful when combined with theory, experimentation or design-based research. The findings create an opportunity to improve feedback in educational technologies and to advance the learning sciences.
These issues will be explored at Formative Feedback in Interactive Learning Environments, a workshop to be held at Artificial Intelligence in Education 2013. Workshop themes:
Feedback content: what to say to the student, and what not to say
Feedback timing, e.g., delayed vs. immediate feedback, feedback on work in progress vs. on complete work, requested vs. proactive feedback
Feedback sequencing, e.g., from general to specific
Form of feedback: discourse properties of feedback, visual presentation, multimodal presentation
Feedback providers: tutoring systems, virtual agents, peer learners, instructors, experts, self-assessment
Outcomes: effects of feedback on current problem performance, next problem performance, transfer, retention, future learning, motivation, affect, achievement orientation
Research methods: analytics / data mining, theory, experimentation, design
Computational models of feedback
Interaction of feedback with learner characteristics, incl. cognitive, metacognitive, affective characteristics, underserved learners, special education learners
Help-seeking behaviors
Interaction of feedback with domain characteristics, incl. feedback in well-defined vs. open-ended problem-solving, design tasks, writing tasks, workplace learning, informal learning
Feedback in learning environments, incl. distance learning, blended learning, MOOCs
Feedback generation: automated, semi-automated, collaborative, social, crowdsourced, adaptation, personalization
Implementation: user interfaces, logging, instrumentation, modularization