Recent PublicationsClicking on the triangle reveals the abstract. Clicking on the title takes you to the publisher's website.
Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (in press). Instruction based on adaptive learning technologies. In R. E. Mayer & P. Alexander (Eds.), Handbook of research on learning and instruction. Routledge.
This chapter does not have an official abstract, but here is an unofficial abstract. In this chapter we examine the widely-held assumption that learning technologies are most effective in supporting learners when they adapt to differences among learners. We also examine the less common assumption that instruction should be adapted to important similarities among learners, such as common hurdles in learning particular subjects. The chapter introduces an Adaptivity Grid to organize the empirical research on adaptivity. Using this framework, we systematically exmine which forms of adaptivity have proven to be effective in empirical research studies. We find substantial evidence that adaptive forms of instruction can be more effective than corresponding non-adaptive instruction. We formulate general conclusions and trends distilled from the empirical literature.
2016 Selected Publications
Aleven, V., Baker, R., Wang, Y., Sewall, J., & Popescu, O. (2016). Bringing non-programmer authoring of intelligent tutors to MOOCs. In J. Haywood, V. Aleven, J. Kay, & I. Roll (Eds.), L@S '16: Work-in-Progress papers of the Third (2016) ACM Conference on Learning @ Scale (pp. 313-316). New York: ACM. doi:10.1145/2876034.2893442
Learning-by-doing in MOOCs may be enhanced by embedding intelligent tutoring systems (ITSs). ITSs support learning-by-doing by guiding learners through complex practice problems while adapting to differences among learners. We extended the Cognitive Tutor Authoring Tools (CTAT), a widely-used non-programmer tool kit for building intelligent tutors, so that CTAT-built tutors can be embedded in MOOCs and e-learning platforms. We demonstrated the technical feasibility of this integration by adding simple CTAT-built tutors to an edX MOOC, "Big Data in Education." To the best of our knowledge, this integration is the first occasion that material created through an open-access non-programmer authoring tool for full-fledged ITS has been integrated in a MOOC. The work offers examples of key steps that may be useful in other ITS-MOOC integration efforts, together with reflections on strengths, weaknesses, and future possibilities.
Aleven, V., McLaren, B. M., Roll, I., & Koedinger, K. R. (2016). Help helps, but only so much: Research on help seeking with intelligent tutoring systems. International Journal of Artificial Intelligence in Education, 26(1), 205-223. doi:10.1007/s40593-015-0089-1
Help seeking is an important process in self-regulated learning (SRL) that may influence learning with intelligent tutoring systems (ITSs). The Help Tutor was a tutor agent that gave in-context, real-time feedback on students' help-seeking behavior, as they worked with an ITS. This feedback helped students so seek help more deliberately, but not to achieve better learning outcomes. The work made a number of contributions, including the creation of a knowledge-engineered, rule-based, executable model of help seeking that can drive tutoring. We review these contributions from a contemporary perspective, with a theoretical analysis, a review of recent empirical literature on help seeking with ITSs, and methodological suggestions. Although we do not view on-demand, principle-based help during tutored problem solving as being as important as we once did, we still view it as helpful under certain circumstances, and recommend that it be included in ITSs. We view the goal of helping students become better self-regulated learners as one of the grand challenges in ITSs research today.
Aleven, V., McLaren, B. M., Sewall, J., van Velsen, M., Popescu, O., Demi, S., Ringenberg, M., & Koedinger, K. R. (2016). Example-tracing tutors: Intelligent tutor development for non-programmers. International Journal of Artificial Intelligence in Education, 26(1), 224-269. doi:10.1007/s40593-015-0088-2
In 2009, we reported on a new Intelligent Tutoring Systems (ITS) technology, example-tracing tutors, that can be built without programming using the Cognitive Tutor Authoring Tools (CTAT). Creating example-tracing tutors was shown to be 4-8 times as cost-effective as estimates for ITS development from the literature. Since 2009, CTAT and its associated learning management system, the Tutorshop, have been extended and have been used for both research and real-world instruction. As evidence that example-tracing tutors are an effective and mature ITS paradigm, CTAT-built tutors have been used by approximately 44,000 students and account for 40 % of the data sets in DataShop, a large open repository for educational technology data sets. We review 18 example-tracing tutors built since 2009, which have been shown to be effective in helping students learn in real educational settings, often with large pre/post effect sizes. These tutors support a variety of pedagogical approaches, beyond step-based problem solving, including collaborative learning, educational games, and guided invention activities. CTAT and other ITS authoring tools illustrate that non-programmer approaches to building ITS are viable and useful and will likely play a key role in making ITS widespread.