The Final Learning-PAL NSF Convergence Accelerator Report is now available here ↗. Read more about Learning-PAL workshop↗
The Center for
Knowledge Communication
We investigate knowledge-based educational systems, integrate theoretical principles into research systems for empirical evaluation and theoretical analysis.
Education Is Not a "One Size Fits All" Proposition
The Center For Knowledge Communication Addresses A Growing Need In Education. Education is an essential part of life, yet it is often approached in a one-size-fits-all manner. Every individual has unique needs and goals, and education should be tailored to those needs and goals. It is important to recognize that no two students are the same, and education should be tailored to each student's needs and strengths.
Our focus is on tailoring activities to meet the individual needs of a diverse student population. The challenge is to meet these needs in a traditional classroom.
Though teachers are responsible for more students than before in the classroom and online, it is possible to produce a private computer tutor for each student and increase the teacher's ability to respond to each individual.
Machine tutors operate like a trusted mentor, speeding through topics that the student grasps easily, concentrating on topics that cause trouble, and never losing patience.
Some Research Challenges Include:
How does a computer
trackstudent collaboration and identify each student's contribution?How does a computer tutor learn to adapt to each student's
learningstyle and cognitive skills?How does a tutor help unmotivated students to
focuson learning?How does the use of technology in the classroom
affectstudent collaboration?What strategies can be used to
maximizestudent collaboration in a technology-driven learning environment?How can teachers use technology to
supportstudent collaboration?How does the use of online tools
impactstudent collaboration?How can teachers use technology to
facilitatepeer-to-peer learning?
Behavioral Studies and Student Modeling
Intelligent tutors are built on models of knowledge that represent the key ideas to be learned, common misconceptions, and how a student's knowledge changes over time. Modeling an individual's knowledge is challenging since students' knowledge is often confused or incomplete.
Combining Technology with Traditional Teaching
These multimedia tutors contain over 1000 problems and are supplemented with data about cognitive features of each student, including variables for individual differences. For example, machine learning was used to modify tutor behavior according to each student's Piagetian developmental stage, spatial ability, or math-fact-retrieval skills.Evaluation results indicate that students with low cognitive skills learn best with concrete representations and manipulatives, and those with higher cognitive skills learn best with abstract or symbolic representations. The effects of gender characteristics in learning have also been measured. Female students spend about 25% more time on hints than male students, perceive a tutor more positively than male students, and are more willing to use the tutor again. Boys with low cognitive development perform worse when they receive abstract or symbolic help while boys with advanced cognitive skills seem to learn better with abstract help than with hints.
The Center for Knowledge Communication Leaders
Dr. Woolf has built many intelligent tutors in collaboration with colleagues in psychology, chemistry, engineering, biology, ecology, geology, education, and medicine. Some of the most promising tutors were built with Research Scientist Ivon Arroyo and included tutors for elementary and high school mathematics.She studied the effects on individual learning differences (e.g., mathematical ability) and group characteristics (e.g., gender) were documented by deploying two tutors - AnimalWatch (arithmetic) and Wayang Outpost (geometry) - among nearly a thousand elementary and high school students.In 2013, the President of the United States Barack Obama awarded Beverly Woolf the Presidential Innovation Fellowship (PIF).Learn more about Dr. Woolf.
Ivon Arroyo is a unique researcher whose expertise spans the fields of learning sciences, computer science, and educational/cognitive psychology. As a leader in the design of innovative technologies for learning and assessment for K-12 students studying mathematics, Ivon's work is revolutionizing the way students learn and understand math.Dr. Arroyo and her research group are also exploring new ways to use technology to facilitate learning and computational thinking. In particular, they are looking into the use of wearable devices and mobile electronic devices to create interactive learning experiences. These devices can be used to design, develop, and play multiplayer physically active embodied math games.Learn more about Arroyo's work on her learning sciences research lab, the Advanced Learning Technologies Lab ↗.
Learning to Teach
Machine learning (ML) techniques are used to model each student's skills and to optimize the selection of problems and hints. During tutoring sessions, the tutoring program can assess a student's skills by considering variables such as prior knowledge and the level of a student's engagement in the tutoring process. Bayesian and data mining techniques help identify a student's skills and predict student reactions to a variety of teaching styles (e.g., present a hint or an example) and to understand how each student learns.Bayesian nets are used to reason about a student's affective state (motivation, engagement, interest, and learning) and to discover links between observable behavior (time spent on hints, number of hints selected) and hidden variables (attitudes and goals). Correlation between observable student activity and survey responses are converted into a network that tests the predictions on the data log of new students.
Beyond Traditional Classrooms
Students are often passive in classrooms; they are not regularly involved in thinking, active learning, problem-solving, or argumentation. In the traditional classroom, teachers ask 95% of the questions, mostly requiring short answers. Traditional classroom methods - lectures, books, multiple-choice exams - lead to passive students and are successful only with the top 25% of students. Liberal use of interactive graphics (3D modeling and interactive character animation) and sound within intelligent tutors help teachers connect with all students.We do not intend for this technology to be used to imitate conventional classroom approaches; rather we focus on challenging traditional teaching and supporting new teaching methods. Intelligent tutors play an essential role in moving education towards more student-centered methods, e.g., team collaboration, case-based inquiry, and apprenticeship; techniques that are nearly impossible to implement without technology.
Behavioral Studies and Student Modeling
As cognitive science and psychology continue to broaden our understanding of how people learn, a real possibility exists to produce a teacher for every student. Thus, content, teaching, assessment, student-teacher relationships, and even the concept of educational institutions may all need to be rethought.Intelligent tutors are built on models of knowledge that represent the key ideas to be learned, common misconceptions, and how a student's knowledge changes over time. Modeling an individual's knowledge is challenging since students' knowledge is often confused or incomplete. While using the computer tutors, students with weak skills benefit the most, seem comfortable requesting hints, make use of help and instruction, and demonstrate improved performance. This is the reverse of the usual findings in the classroom, where higher achieving students are most likely to request help.
Using ML techniques, we can predict a student's level of engagement with 80-90% accuracy, and how a student will perform on each problem with 70% accuracy. For collaborative research opportunities and any other inquiries, reach out to us
NEWS
Latest News from the CKC
October 2022
Beverly Woolf, PhD hosts the Convergence Accelerator Ideation Workshop supported by the National Science Foundation’s (NSF) Convergence Accelerator Program (Con-Accel). The primary objective is to develop a convincing and compelling rationale for NSF to establish a Con-Accel funding track in 2023 to support creation of smart and integrated platforms, devices and processes for education technology.
Learn more about the Learning-PAL workshop ↗.
June 2010
Best Paper Award EDM 2010.
Arroyo, I., Mehranian, H., & Woolf, B. P. (2010, June). Effort-based tutoring: An empirical approach to intelligent tutoring. In Educational data mining 2010.
June 2010
Best Paper Award AIED 2009 - Ivon Arroyo Best Paper Award.
Arroyo, I., Cooper, D. G., Burleson, W., Woolf, B. P., Muldner, K., & Christopherson, R. (2009). Emotion sensors go to school. In Artificial intelligence in education (pp. 17-24). Ios Press.
PUBLICATIONS
Recent Publications
Woolf, B., Ghosh, A., Lan, A., Zilberstein, S., Juravizh, T., Cohen, A., & Geho, O. (2020). AI-Enabled Training in Manufacturing Workforce Development. University of Massachusetts Amherst.
Joshi, A., Allessio, D., Magee, J., Whitehill, J., Arroyo, I., Woolf, B., ... & Betke, M. (2019, May). Affect-driven learning outcomes prediction in intelligent tutoring systems. In 2019 14th IEEE international conference on automatic face & gesture recognition (FG 2019) (pp. 1-5). IEEE.
Allessio, D., Woolf, B., Wixon, N., Sullivan, F. R., Tai, M., & Arroyo, I. (2018, June). Ella me ayudó (she helped me): Supporting hispanic and english language learners in a math its. In International Conference on Artificial Intelligence in Education (pp. 26-30). Springer, Cham.
Wixon, N., Woolf, B., Schultz, S., Allessio, D., & Arroyo, I. (2018, June). Microscope or Telescope: Whether to Dissect Epistemic Emotions. In
International Conference on Artificial Intelligence in Education (pp. 384-388). Springer, Cham.
Arroyo, I., Wixon, N., Allessio, D., Woolf, B., Muldner, K., & Burleson, W. (2017, June). Collaboration improves student interest in online tutoring. In international conference on artificial intelligence in education (pp. 28-39). Springer, Cham.
Karumbaiah, S., Lizarralde, R., Allessio, D., Woolf, B., Arroyo, I., & Wixon, N. (2017). Addressing Student Behavior and Affect with Empathy and Growth Mindset. International Educational Data Mining Society.
Past Publications:
Wixon, N., Schultz, S., Muldner, K., Allessio, D., Burleson, W., Woolf, B., & Arroyo, I. (2016, July). Internal & external attributions for emotions within an ITS. In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization (pp. 311-312).
“When the Going Gets Tough…”: Challenge, Emotions, & Difference of Perspective
Schultz, S. E., Wixon, N., Allessio, D., Muldner, K., Burleson, W., Woolf, B., & Arroyo, I. (2016, June). Blinded by science?: Exploring affective meaning in students’ own words. In International Conference on Intelligent Tutoring Systems (pp. 314-319). Springer, Cham.
Wixon, M., Allessio, D., Ocumpaugh, J., Woolf, B. P., Burleson, W., & Arroyo, I. (2015). La Mort du Chercheur: How well do students' subjective understandings of affective representations used in self-report align with one another's, and researchers'?. In EDM (Workshops).
Schultz, S. E., Wixon, N., Allessio, D., Muldner, K., Burleson, W., Woolf, B., & Arroyo, I. (2016, June). Blinded by science?: Exploring affective meaning in students’ own words. In International Conference on Intelligent Tutoring Systems (pp. 314-319). Springer, Cham.
Woolf, B. P., & Arroyo, I. (2015). A mentor for every student: One challenge for instructional software. IBM Journal of Research and Development, 59(6), 9-1.
Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education. AI magazine, 34(4), 66-84.
Arroyo, I., Woolf, B. P., Burelson, W., Muldner, K., Rai, D., & Tai, M. (2014). A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. International Journal of Artificial Intelligence in Education, 24(4), 387-426.
BEVERLY WOOLF
Meet the Professor
Beverly Park Woolf, PhD, is a Research Professor in the Computer Science Department of the University of Massachusetts Amherst. She is the Director of the Center for Knowledge Communication. Many of the three-dimensional graphics and multimedia classes at the University of Massachusetts owe their beginning to Dr. Woolf's efforts to offer students the opportunity to expand both intellectual and practical skills.
Dr. Woolf's research focuses on building systems to train, explain, and advise users effectively. Extended multimedia capabilities are integrated with knowledge about the user, domain, and dialogue to produce real-time performance support and on-demand advisory and tutoring systems. The tutoring systems use intelligent interfaces, inferencing mechanisms, cognitive models, and modifiable software to improve a computer's communicative abilities. These systems have been tested with learners, trainers, and other client bases, deployed in education and industry and demonstrated in more than 50 American industrial, military and academic sites and 15 foreign countries.
Her most recent book is Building Intelligent Interactive Tutors ↗, Student-Centered Strategies for Revolutionizing E-Learning, Published by Elsevier & Morgan Kaufmann, 2008.
For more information about past and present multimedia classes offered by the Center for Knowledge Communication, please use our contact form.
IVON ARROYO
Meet the Professor
Dr. Arroyo holds a Licenciatura en Informatica Universidad Blas Pascal - Computer Science from Cordoba, Argentina. She received both her computer science masters degree (2000) and an educational doctoral degree (2003) from the University of Massachusetts Amherst.
She is a Fulbright Fellow and an elected member of the executive committee of the International Society of Artificial Intelligence in Education. Her current work focuses primarily on Wayang Outpost, a geometry tutor for middle and high school students.
In her interdisciplinary role, she has carried out top research at the forefront of education, computer science, and psychology, co-authoring research articles at the forefront of the three disciplines.
From the education perspective, she has researched and created learning software for mathematics with multimedia, and worked closely with thousands of K-12 students and teachers, deployed software in public schools, while trying understand how students best learn and perceive mathematics with interactive math tutoring software, and how to support teachers in their teaching process via digital assessment tools.
From the computer science perspective, she has created artificially intelligent tutoring software that models students' knowledge and affective states, infers them from student behaviors and physiological sensors, and which acts upon those students states, as students use the software. She has used data mining methods to learn a variety of student states from past student data logs.
From the psychology perspective, she has analyzed developmental gender differences in the use and benefit of math tutoring software, cognitive development issues in relation to the best representations to use while teaching mathematics, and memory retrieval studies where the training of speed of retrieval of basic math facts help students increase working memory capacity and succeed in complex math tasks. Ivon Arroyo is a PI or co-PI for NSF and Department of Education research grants that attempt to find principles for the design of digital learning environments for STEM that enhance affective and cognitive outcomes, with an emphasis on girls and students with learning disabilities.
Learn more about Arroyo's Advanced Learning Technologies Lab ↗.
CONTACT
How Can We Help?
Thank you for your interest in the Center for Knowledge Communication. Please use the form below to get in touch with us.