Award Abstract #0411886
Achieving Motivational and Cognitive Outcomes in Mathematics Using Enhanced Intelligent Tutoring Technology

NSF Org:	DRL Division of Research on Learning in Formal and Informal Settings (DRL)
Initial Amendment Date:	June 18, 2004
Latest Amendment Date:	March 27, 2006
Award Number:	0411886
Award Instrument:	Continuing grant
Program Manager:	John Cherniavsky DRL Division of Research on Learning in Formal and Informal Settings (DRL) EHR Directorate for Education & Human Resources
Start Date:	July 1, 2004
Expires:	June 30, 2008 (Estimated)
Awarded Amount to Date:	$1217641
Investigator(s):	William Johnson johnson@isi.edu (Principal Investigator) Carole Beal (Co-Principal Investigator)
Sponsor:	University of Southern California University Park Los Angeles, CA 90089 213/740-7762
NSF Program(s):	RESEARCH ON LEARNING & EDUCATI
Field Application(s):	0116000 Human Subjects
Program Reference Code(s):	SMET, 9177
Program Element Code(s):	1666

ABSTRACT

Much research in education now points to the importance of both cognitive and

motivational processes in determining learning outcomes. Yet we do not yet understand

exactly how these processes interact within the individual learner over time to produce

specific learning outcomes, and how different forms of instruction and tutoring can

influence the interaction of cognition and motivation to facilitate learning. Advanced

learning technologies such as intelligent tutoring systems typically focus on cognitive

issues, and do not yet address motivational issues. Thus there is a major gap between

educational practice, as supported by learning technologies, and research in cognitive and

motivational aspects of human learning.

The goal of the proposed project is to investigate the relations of learner

engagement, cognitive processes, and scaffolding strategies in producing specific

learning outcomes in mathematics. It brings together an interdisciplinary team of

specialists in psychology, education, and information technology to pursue this work. The

project will employ educational software designed to enable us to address and measure

cognitive and motivational factors in systematic ways. It will significantly extend

previous work on intelligent tutoring systems and computer-based learning assistants to

assess in real time the attention and motivation of individual learners, infer their level of

engagement, and estimate their domain knowledge. The research results will be obtained

in realistic learning conditions: public high school classrooms in Los Angeles, with

student populations reflecting the ethnic, linguistic and economic diversity of urban

California. The software will make instructional decisions based on decision rules used

by expert human tutors who consider motivational and cognitive goals to optimize

learning outcomes. Instructional strategies will also be instantiated in the form of a

responsive animated virtual character, to learn if the presence of a human-like

pedagogical agent influences the learner.s engagement as well as learning outcomes in

mathematics. Classroom teachers will be able to customize the instruction through

innovative tools to view continually updated assessments and refine pedagogical

decisions made by the software. Rich learner data sets will be automatically collected as

students work on mathematics problems, and will provide an integrated evaluation linked

to specific learning outcomes, including word problem solving and transfer to novel,

challenging problems embedded in real-world STEM contexts. To accomplish this goal,

the researchers will build on the results of two projects developed with prior NSF support: an

empirically validated focus of attention tracking system, and an intelligent tutoring

system (ITS) designed specifically to enhance math skills in students who have been

traditionally under-represented in STEM fields of study.

The results of the project will have broad societal impact through providing

individualized, effective web-based instruction in STEM-related mathematics that will be

freely accessible to students in any high school with access to the Internet. We anticipate

that the impact will be greatest among students who currently have motivational

difficulties with mathematics, including women and underrepresented groups. The

intellectual merit of the project will be a deeper understanding of the very nature of

learning processes through the ability to model in detail and in real time the interaction of

cognition and engagement in the individual learner, and to observe changes in the

learner.s knowledge as a function of instructional history.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Beal, C. R., & Qu, L.. "Relating machine estimates of students' learning goals to learning outcomes: A DBN," Proceedings of the 13th International Conference on Artificial Intelligence and Education, v.158, 2007, p. 111.

Beal, C. R., Mitra, S., & Cohen, P. R.. "Modeling learning patterns of students with a tutoring system using Hidden Markov Models," Proceedings of the 13th International Conference on Artificial Intelligence and Education, v.158, 2007, p. 238.

Beal, C. R., Walles, R., Arroyo, I., & Woolf, B. P.. "On-line tutoring for math achievement testing: A controlled evaluation.," Journal of Interactive Online Learning, v.6, 2007, p. 43-55.

Carole R Beal & Hyokyeong Lee. "Creating a pedagogical model that uses student self reports of motivation and mood to adapt ITS instruction," Proceedings of the Workshop on Motivation and Affect in ITS, AI-ED 2005, IOS Press., 2005, p. 39.

Carole R. Beal. "Adaptive user displays for intelligent tutoring software," Cyberpsychology and Behavior, v.7, 2004, p. 689.

Carole R. Beal & Paul Cohen. "Computational methods for evaluating student and group learning histories in intelligent tutoring systems," In C. Looi, G. McCalla, B. Bredeweg, & J. Breuker (Eds.), Artificial Intelligence in Education: Supporting learning through intelligent and socially-informed technology., 2005, p. 80.

Carole R. Beal, Erin Shaw, Yuan-Chun Chui, Hyokyeong Lee, Hannes Vilhjalmsson & Lei Qu. "Enhancing ITS instruction with integrated assessments of learner mood, motivation, and gender," In C. Looi, G. McCalla, B. Bredeweg, & J. Breuker (Eds.), Artificial Intelligence in Education: Supporting learning through intelligent and socially-informed technology, Amsterdam: IOS Press., 2005, p. 750.

Woolf, B. P., Arroyo, I., Murray, T., & Beal, C. R.. "Gender and cognitive differences in help effectiveness during problem solving," International Journal of Technology, Instruction, Cognition and Learning, v.3, 2006, p. 89-95.

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