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45. Multilevel Learner Modeling in Training Environments for Complex Decision Making

Author

Gautam Biswas, Naveeduddin Mohammed, Robert A. Sottilare, Ramkumar Rajendran, Benjamin Goldberg, Michael Hoffman, and Keith Brawner

Subjects
Scheme (programming language), UrbanSim, Computer science, 05 social sciences, General Engineering, 050301 education, Metacognition, Face (sociological concept), Cognition, 02 engineering and technology, Computer Science Applications, Education, Human–computer interaction, ComputingMilieux_COMPUTERSANDEDUCATION, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Cognitive skill, 0503 education, computer, computer.programming_language
Abstract

Intelligent learning environments can be designed to support the development of learners’ cognitive skills, strategies, and metacognitive processes as they work on complex decision-making and problem-solving tasks. However, the complexity of the tasks may impede the progress of novice learners. Providing adaptive feedback to learners who face difficulties requires learner modeling approaches that can identify learners’ proficiencies and the difficulties they face in executing required skills, strategies, and metacognitive processes. This paper discusses a multilevel hierarchical learner modeling scheme that analyzes and captures learners’ cognitive processes and problem-solving strategies along with their performance on assigned tasks in a game-based environment called UrbanSim that requires complex decision making for dealing with counterinsurgency scenarios. As the scenario evolves in a turn-by-turn fashion, UrbanSim evaluates the learners’ moves using a number of performance measures. Our learner modeling scheme interprets the reported performance values by analyzing the learners’ activities captured in log files to derive learners’ proficiencies in associated cognitive skills and strategies, and updates the learner model. We discuss the details of the learner modeling algorithms in this paper, and then demonstrate the effectiveness of our approach by presenting results from a study we conducted at Vanderbilt University.

Published
2020

47. Adaptive Instructional Systems : 6th International Conference, AIS 2024, Held As Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29–July 4, 2024, Proceedings

Author

Robert A. Sottilare, Jessica Schwarz, Robert A. Sottilare, and Jessica Schwarz

Subjects
User interfaces (Computer systems), Human-computer interaction, Education—Data processing, Social sciences—Data processing, Image processing, Computer vision, Data mining
Abstract

This book constitutes the refereed proceedings of 6th International Conference on Adaptive Instructional Systems, AIS 2024, held as part of the 26th International Conference, HCI International 2024, which took place in Washington, DC, USA, during June 29-July 4, 2024. The total of 1271 papers and 309 posters included in the HCII 2024 proceedings was carefully reviewed and selected from 5108 submissions. The HCII-AIS 2024 contributions have been organized in the following topical sections: Designing and developing adaptive instructional systems; adaptive learning experiences; AI in adaptive learning.

Published
2024

48. Scaling Adaptive Instructional System (AIS) Architectures in Low-Adaptive Training Ecosystems

Author

Keith Brawner and Robert A. Sottilare

Subjects
Software, Horizontal and vertical, Computer science, Human–computer interaction, business.industry, Interface (computing), business, Training (civil), Scaling, Common operational picture, Variety (cybernetics), Term (time)
Abstract

This paper reviews horizontal and vertical scaling methodologies for adaptive instructional system (AIS) software architectures. The term AIS refers to any instructional approach that accommodates individual differences to facilitate and optimize the acquisition of knowledge and/or skills. The authors propose a variety of scaling methods to enhance the interaction between AISs and low-adaptive training ecosystems with the goal of increasing adaptivity and thereby increasing learning and performance. Typically, low-adaptive training systems only accommodate differences in the learner’s in-situ performance during training and do not consider the impact of other factors (e.g., emotions, prior knowledge, goal-orientation, or motivation) that influence learning. AIS architectures such as the Generalize Intelligent Framework for Tutoring (GIFT) can accommodate individual differences and interact with low-adaptive training ecosystems to model a common operational picture of the training relative. These capabilities enable AISs to track progress toward learning objectives and to intervene and adapt the training ecosystem to needs and capabilities of each learner. Finding new methods to interface AISs with a greater number of low-adaptive training ecosystems will result in more efficient and effective instruction.

Published
2021

49. HCI International 2023 – Late Breaking Papers : 25th International Conference on Human-Computer Interaction, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings, Part VII

Author

Panayiotis Zaphiris, Andri Ioannou, Robert A. Sottilare, Jessica Schwarz, Fiona Fui-Hoon Nah, Keng Siau, June Wei, Gavriel Salvendy, Panayiotis Zaphiris, Andri Ioannou, Robert A. Sottilare, Jessica Schwarz, Fiona Fui-Hoon Nah, Keng Siau, June Wei, and Gavriel Salvendy

Subjects
User interfaces (Computer systems), Human-computer interaction
Abstract

This seven-volume set LNCS 14054-14060 constitutes the proceedings of the 25th International Conference, HCI International 2023, in Copenhagen, Denmark, in July 2023. For the HCCII 2023 proceedings, a total of 1578 papers and 396 posters was carefully reviewed and selected from 7472 submissions. Additionally, 267 papers and 133 posters are included in the volumes of the proceedings published after the conference, as “Late Breaking Work”. These papers were organized in the following topical sections: HCI Design and User Experience; Cognitive Engineering and Augmented Cognition; Cultural Issues in Design; Technologies for the Aging Population; Accessibility and Design for All; Designing for Health and Wellbeing; Information Design, Visualization, Decision-making and Collaboration; Social Media, Creative Industries and Cultural Digital Experiences; Digital Human Modeling, Ergonomics and Safety; HCI in Automated Vehicles and Intelligent Transportation; Sustainable GreenSmart Cities and Smart Industry; eXtended Reality Interactions; Gaming and Gamification Experiences; Interacting with Artificial Intelligence; Security, Privacy, Trust and Ethics; Learning Technologies and Learning Experiences; eCommerce, Digital Marketing and eFinance.

Published
2023

50. Adaptive Instructional Systems : 5th International Conference, AIS 2023, Held As Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings

Author

Robert A. Sottilare, Jessica Schwarz, Robert A. Sottilare, and Jessica Schwarz

Subjects
User interfaces (Computer systems), Human-computer interaction
Abstract

This book constitutes the refereed proceedings of the 5th International Conference, AIS 2023, held as part of the 25th International Conference, HCI International 2023, which was held virtually in Copenhagen, Denmark in July 2023.The total of 1578 papers and 396 posters included in the HCII 2023 proceedings was carefully reviewed and selected from 7472 submissions. The AIS 2023 proceeding helps to understand the theory and enhance the state-of-practice for a set of technologies (tools and methods) called adaptive instructional systems (AIS). AIS are defined as artificially intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, preferences, and interests of each individual learner or team in the context of domain learning objectives.

Published
2023

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