No Skill Left Behind: Intelligent Tutoring Systems Enable a New Paradigm in Learning

Hatem M. Wasfy; Tamer M. Wasfy; Jeanne Peters; Riham M. Mahfouz

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Conference: 2012 ASEE Annual Conference & Exposition
Location: San Antonio, Texas
Publication Date: June 10, 2012
Start Date: June 10, 2012
End Date: June 13, 2012
ISSN: 2153-5965
Conference Session: CoED General Technical Session I
Tagged Division: Computers in Education
Page Count: 11
Page Numbers: 25.977.1 - 25.977.11
DOI: 10.18260/1-2--21734
Permanent URL: https://peer.asee.org/21734
Download Count: 515

Paper Authors

biography

Hatem M. Wasfy Advanced Science and Automation Corp.

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Hatem Wasfy is the President of Advanced Science and Automation Corp. (ASA), a company that specializes in the development of online virtual learning environments, and advanced engineering simulations. He has helped design several interactive learning environments that include a CNC machining course, a centrifugal pump maintenance course, an undergraduate physics course, and a welding course. He received a B.S. (1994) and an M.S. (1996) in mechanical engineering from the American University in Cairo. Wasfy’s research interests include advanced learning systems, cavitation modeling, computational fluid dynamics, internal combustion engine modeling and design, and AI rule-based expert systems.

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Tamer M. Wasfy Indiana University-Purdue University, Indianapolis

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Tamer Wasfy received a B.S. (1989) in mechanical engineering and an M.S. (1990) in materials engineering from the American University in Cairo, and an M.Phil. (1993) and Ph.D. (1994) in mechanical engineering from Columbia University. He worked as a Research Scientist at the Department of Mechanical Engineering, Columbia University (1994-1995) and at the University of Virginia at NASA Langley Research Center (1995-1998). Wasfy is an Associate Professor at the Mechanical Engineering Department at Indiana University-Purdue University Indianapolis (IUPUI). Dr. Wasfy is also the founder and chairman of Advanced Science and Automation Corp. (founded in 1998) and AscienceTutor (founded in 2007). Wasfy's research and development areas include: flexible multibody dynamics, finite element modeling of solids and fluids, fluid-structure interaction, belt-drive dynamics, tires mechanics/dynamics, ground vehicle dynamics, visualization of numerical simulation results, engineering applications of virtual-reality and artificial intelligence. He authored and co-authored more than 70 peer-reviewed publications and gave more than 65 presentations at international conferences and invited lectures in those areas. He received two ASME best conference paper awards as first author. He is the software architect for the DIS, IVRESS, and LEA software systems, which are used by industry, government agencies, and academic institutions. Wasfy is a member of ASME, AIAA, SAE, and ASEE.

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Jeanne Peters Advanced Science and Automation Corp.

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Jeanne Peters received a B.A. in math/computer science from the College of William and Mary. She worked at the NASA Langley Research Center in Hampton, Va. for more than 20 years as a Senior Programmer/Analyst for George Washington University, University of Virginia, and Old Dominion University. She co-authored more than 70 journal and conference papers in the areas of: computational mechanics, finite element method, shells/plates, composite material panels, and tires. She has also worked on numerous projects to create advanced engineering design and learning environments which include multimodal user interfaces for space systems. As Vice President of Information Technology, Peters directs the development of advanced virtual reality applications, including scientific visualization applications and web-based multimedia education/training applications.

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Riham M. Mahfouz Thomas Nelson Community College

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Riham Mahfouz is the Department Head of the Chemistry Department at the Thomas Nelson Community College (TNCC), where she teaches and serves as Course Coordinator for the following courses: preparatory chemistry, organic chemistry, and online college chemistry. Mahfouz has extensive training and experience in developing online courses. She has developed online college chemistry courses using the ASSURE Model of instructional design and the standards created by the Quality Matters faculty-centered peer review process for certifying the quality of online courses.

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Abstract

No Skill Left Behind: Intelligent Tutoring Systems Enable a New Paradigm in LearningTraditional education only assesses the average proficiency of students over an entire course, nottheir actual proficiency in every course topic. Hence even a student who finishes a course withan A grade might have a failing proficiency level in one or more critical course topics.Furthermore, during the course, struggling students are not given enough chance to improve theirperformance due to the limited amount of time during which the course is offered, limitations ontheir access to the course instructor, and not being offered timely remedial learning in topics theytook before and either never fully became proficient in, or simply forgot them. Finally after astudent finishes a given course, there is no effective mechanism to ensure that the studentremains fully proficient in all the skills he/she has learned, or to remediate any skills that he/sheforgets.In this paper, a computerized Intelligent Tutoring System (ITS) that addresses the abovementioned issues is presented. The ITS uses automated course delivery, formative assessments,and non-linear course navigation. The automated course delivery employs multimodalinstruction delivery using a computer voice that is generated using text-to-speech, and writtentext that is highlighted in sync with the voice. The course delivery is highly visual and includes2D and virtual reality 3D graphics, animations, and interactive simulations that are synchronizedwith the course delivery. The ITS provides unlimited online personalized tutoring that adjusts thepace of instruction to the student’s needs. The ITS contains an ontology of all the course topics(the course model) with interdependency scores assigned to each topic connection that reflectsthe level of dependence of every course topic on preceding course topics. The ITS also has astudent model that keeps track of the student’s declarative and intuitive proficiency scores inevery course topic. Each skill in the student model is also assigned an expiration date that isbased on the skill’s difficulty, criticality, and the last time the student successfully used it.The ITS continuously uses formative assessments to gauge the student’s performance and adaptthe course delivery as the student progresses through the course. In case of performance failurein the assessment of any topic, the ITS uses probability analysis to determine which topic incourse’s ontology caused the failure based on the topic interdependency scores from the coursemodel and on the student model. The ITS then administers an assessment of that later topic, andprovides immediate remedial learning to correct the failure if needed. The student and coursemodels can encompass more than one course, up to the entire student curriculum. Thus the ITScan administer an entire curriculum seamlessly, and the ITS’s remedial actions can cross courseboundaries into adjacent courses and disciplines. The ITS ensures that the student remains fullyproficient in every curriculum skill by continuously assessing and remediating these skills duringcurriculum delivery, and by automatically assessing previously learned skills once theirexpiration dates are reached.

Citation
Format

Wasfy, H. M., & Wasfy, T. M., & Peters, J., & Mahfouz, R. M. (2012, June), No Skill Left Behind: Intelligent Tutoring Systems Enable a New Paradigm in Learning Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. 10.18260/1-2--21734

TY  - CPAPER
AB  -            No Skill Left Behind: Intelligent Tutoring Systems Enable a                           New Paradigm in LearningTraditional education only assesses the average proficiency of students over an entire course, nottheir actual proficiency in every course topic. Hence even a student who finishes a course withan A grade might have a failing proficiency level in one or more critical course topics.Furthermore, during the course, struggling students are not given enough chance to improve theirperformance due to the limited amount of time during which the course is offered, limitations ontheir access to the course instructor, and not being offered timely remedial learning in topics theytook before and either never fully became proficient in, or simply forgot them. Finally after astudent finishes a given course, there is no effective mechanism to ensure that the studentremains fully proficient in all the skills he/she has learned, or to remediate any skills that he/sheforgets.In this paper, a computerized Intelligent Tutoring System (ITS) that addresses the abovementioned issues is presented. The ITS uses automated course delivery, formative assessments,and non-linear course navigation. The automated course delivery employs multimodalinstruction delivery using a computer voice that is generated using text-to-speech, and writtentext that is highlighted in sync with the voice. The course delivery is highly visual and includes2D and virtual reality 3D graphics, animations, and interactive simulations that are synchronizedwith the course delivery. The ITS provides unlimited online personalized tutoring that adjusts thepace of instruction to the student’s needs. The ITS contains an ontology of all the course topics(the course model) with interdependency scores assigned to each topic connection that reflectsthe level of dependence of every course topic on preceding course topics. The ITS also has astudent model that keeps track of the student’s declarative and intuitive proficiency scores inevery course topic. Each skill in the student model is also assigned an expiration date that isbased on the skill’s difficulty, criticality, and the last time the student successfully used it.The ITS continuously uses formative assessments to gauge the student’s performance and adaptthe course delivery as the student progresses through the course. In case of performance failurein the assessment of any topic, the ITS uses probability analysis to determine which topic incourse’s ontology caused the failure based on the topic interdependency scores from the coursemodel and on the student model. The ITS then administers an assessment of that later topic, andprovides immediate remedial learning to correct the failure if needed. The student and coursemodels can encompass more than one course, up to the entire student curriculum. Thus the ITScan administer an entire curriculum seamlessly, and the ITS’s remedial actions can cross courseboundaries into adjacent courses and disciplines. The ITS ensures that the student remains fullyproficient in every curriculum skill by continuously assessing and remediating these skills duringcurriculum delivery, and by automatically assessing previously learned skills once theirexpiration dates are reached.
AU  - Hatem M. Wasfy
AU  - Tamer M. Wasfy
AU  - Jeanne Peters
AU  - Riham M. Mahfouz
CY  - San Antonio, Texas
DA  - 2012/06/10
PB  - ASEE Conferences
TI  - No Skill Left Behind: Intelligent Tutoring Systems Enable a New Paradigm in Learning
UR  - https://peer.asee.org/21734
DO  - 10.18260/1-2--21734
ER  -