| iHATS: Exploring Student Behavior Across Human and Automated Tutoring Systems: Foundations for a Blended Approach
(Role: Principal Investigator; Agency: DoD; Amount: $263,457.00; Period: 2015-2017)
Using existing tools and capabilities, developed in large part previously, we propose a largescale data mining project aimed at extracting useful information and knowledge from the combined data set. In particular, we propose to gain answers to the following research
RQ1. What tutorial tactics, strategies and metastrategies used by human tutors result in the greatest improvement in student learning, as measured by improvement in AMP performance from that prior to the interaction with human tutors to that after the interaction with human tutors? Are there characteristics (such as a particular unit of the curriculum, prior performance, or affective state) that help determine the most appropriate tutorial interaction?
RQ2. What characteristics of student interaction within AMP (like the use of hints) or student affective state (such as frustration or boredom, as inferred from student interactions with AMP) increase or decrease the likelihood that the student would seek assistance from human tutors?
| Learning from A Big Human Tutoring Service Database
(Role: Principal Investigator; Agency: DoD, Amount: $557,656.00; Period: 2014-2015)
The goal of this project is to automatically discover successful instructional strategies in the form of
sequences of tutorial dialogue moves from raw tutorial conversations between professional human tutors
and students. We apply unsupervised machine learning techniques to learn from more than 10
million tutorial session transcripts (obtained from a commercial online tutoring service) successful
tutorial strategies. These patterns could then be re-used by state-of-the-art educational systems to
improve their training effectiveness.
| DRK-12: Developing and Testing the Internship-inator, a Virtual Internship in STEM
(Role: Co-Principal Investigator; Agency: NSF; Amount: $999,982.00; Period: 2014-2018)
|The goal is to develop the Internship-inator as authorware that will enable STEM content developers to design or modify virtual internships to address different audiences, topics, or purposes without requiring significant prior expertise in computer programming or educational game development. This will facilitate research on authorware design for STEM learning tools more generally as well as research on the effects of virtual internship design and implementation on STEM learning.|
| DeepTutor: An intelligent tutoring system based on deep language and discourse
processing and advanced tutoring strategies
(Role: Principal Investigator; Agency: Institute for Education Sciences; Amount: $1,650,272.00; Period: 2010-2014)
DeepTutor is an advanced intelligent tutoring system that fosters students' deep understanding of complex science topics by using recent advances in science education research, called Learning Progressions, and deep natural language and discourse processing techniques. The emphasis on quality interaction and instruction is what sets DeepTutor apart from previous computer tutors with natural language interaction developed during the last few decades.
Quality interaction is possible in DeepTutor through the use of a novel, state-of-the-art natural language-based knowledge representation, called the latent semantic logic form or shortly the semantic logic form (SLF), and advanced dialogue management techniques that embed novel conversational goals such as perfect grounding at every turn.
Quality instruction in DeepTutor is driven by recent advances in science education research called learning progressions (LPs). LPs capture the natural sequence of mental models and mental model shifts students go through while mastering a topic. DeepTutor can be used as a means to refine and validate LPs. DeepTutor is the first tutoring system that integrates learning progressions.
Based on these theoretical, conceptual, and technological advances, DeepTutor is expected to provide accurate assessment, better communication, and advanced tutoring and instructional strategies; this will result in higher quality interaction between computer tutor and tutee and therefore increased effectiveness on learning gains beyond the interactivity plateau (see Kurt vanLehn's publications on the interactivity plateau).
To learn more about DeepTutor click here.
| MetaTutor: Contextual Research – Empirical Research -Detecting, Tracking, and Modeling
Cognitive, Affective, and Meta-cognitive Regulatory Processes to Optimize Learning
(Role: Co-Principal Investigator; Agency: NSF; Amount: $1,496.507.00; Period: 2010-2013)
This 3-year grant focuses on examining the effectiveness of using
animated pedagogical agents (APAs) as external regulatory agents
designed to foster middle school and college students' understanding
of complex and challenging science topics (e.g., the circulatory
system). Contemporary cognitive and educational research provides
evidence that the potential of computer-based learning environments
for facilitating learning may be severely undermined by students'
inability to regulate several aspects of the learning. For example,
students should regulate key cognitive, metacognitive, motivational,
social, and affective processes in order to learn about complex and
challenging science topics. This research will be conducted in the
context of a mixed-initiative intelligent tutoring system called
AutoTutor that simulates the discourse patterns and pedagogical
strategies of human tutors. The focus of our grant is on conducting
interdisciplinary research examining: (1) the role of embedded
animated pedagogical agents in collecting data of the complex
interactions between cognitive and metacognitive processes during
learning about complex science topics with AutoTutor; (2) the
effectiveness of animated pedagogical agents as external regulating
agents used to detect, trace, model, and foster students'
self-regulatory processes during learning about complex science topics
with AutoTutor; and (3) the effectiveness of scaffolding methods
delivered by animated pedagogical agents in facilitating middle school
and college students' self-regulated learning about complex science
topics with AutoTutor.
To read more about MetaTutor click here.