Using Applied Behavior Analysis in Software to help Tutor Individuals with Autism Spectrum Disorder

There are currently many tutoring software systems which have been designed for neurotypical children. These systems cover academic topics such as reading and math, and are made available through various technological mediums. The majority of these systems were not designed for use by children with special needs, in particular those who are diagnosed with Autism Spectrum Disorder. Since the 1970's, studies have been conducted on the use of Applied Behavior Analysis to help autistic children learn [1]. This teaching methodology is proven to be very effective, with many patients having their diagnosis of autism dropped after a few years of treatment. With the advent of ubiquitous technologies such as mobile devices, it has become apparent that these devices could also be used to help tutor autistic children on academic subjects such as reading and math. Though the delivery of tutoring material must be made using Applied Behavior Analysis techniques, given that ABA therapy is currently the only form of treatment for Autism Spectrum Disorder endorsed by the US Surgeon General [2], which further makes the case for incorporating it into an academics tutoring system tailored for autistic children. In this paper, we present a mobile software system which can be utilized to tutor children who are diagnosed with Autism Spectrum Disorder in the subjects of reading and math. The software makes use of Applied Behavior Analysis techniques such as a Token Economy system, visual and audible reinforcers, and generalization. Furthermore, we explore how combining Applied Behavior Analysis and technology, could help extend the reach of tutoring systems to these children.Comment: 8 pages, 7 figure

The development and analysis of extended architecture model for intelligent tutoring systems

Intelligent Tutoring Systems (ITS) are computer programs that use leamers" knowledge level to providing indívidualized education. ITS research has successfully delivered systems efficiently supporting one-to-one tutoring. Most of these systems are actively used in real-worid settings and have even contributed to changing traditional education curricula. Instructional activities, learning examples, exploring interactive simulations and playing educational games can benefit from individualized computer-based assistance. To enhance ongoing research related to the improvement of tutoring, we present an extended knowledge mode! including besides the standard modules a common shared database and knowledge-based background, too. The external databases can improve the guality of the behavior models both in tutor and student models. The Python programming language and OWL are efficient tools to combine the ontology management and machine leaming functions to develop ITS systems. In this Paper, we survey ITS technologies andpresent a novel extended architecture model for Intelligent e-Tutoring Systems

Trying to Reduce Gaming Behavior by Students in Intelligent Tutoring Systems

Student gaming behavior in intelligent tutoring systems (ITS) has been correlated with lower learning rates. The goal of this work is to identify such behavior, produce interventions to discourage this behavior, and by doing so hopefully improve the learning rate of students who would normally display gaming behavior. Detectors have been built to identify gaming behavior. Interventions have been designed to discourage the behavior and their evaluation is discussed

Educational Software for Off-Task Behavior

Off-task behavior is a problem currently facing intelligent tutoring systems as well as traditional classrooms. There are a number of reasons why students go off-task, and likewise, a number of ways for them to do so. The goals of this project were to (1) develop a potential off-task intervention method, and (2) implement an off-task detector in an existing intelligent tutoring program, which was already capable of detecting and responding to students who were gaming the system

Computational approaches to emotional decision making

Findings on the role that emotion plays in human behavior have transformed Artificial Intelligence computations. Modern research explores how to simulate more intelligent and flexible systems. Several studies focus on the role that emotion has in order to establish values for alternative decision and decision outcomes. For instance, Busemeyer et al. (2007) argued that emotional state affects the subjectivity value of alternative choice. However, emotional concepts in these theories are generally not defined formally and it is difficult to describe in systematic detail how processes work. In this sense, structures and processes cannot be explicitly implemented. Some attempts have been incorporated into larger computational systems that try to model how emotion affects human mental processes and behavior (Becker-Asano & Wachsmuth, 2008; Marinier, Laird & Lewis, 2009; Marsella & Gratch, 2009; Parkinson, 2009; Sander, Grandjean & Scherer, 2005). As we will see, some tutoring systems have explored this potential to inform user models. Likewise, dialogue systems, mixed-initiative planning systems, or systems that learn from observation could also benefit from such an approach (Dickinson, Brew & Meurers, 2013; Jurafsky & Martin, 2009). That is, considering emotion as interaction can be relevant in order to explain the dynamic role it plays in action and cognition (see Boehner et al., 2007).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Intelligent Tutoring Systems for Generation Z's Addiction

As generation Z's big data is flooding the Internet through social nets, neural network based data processing is turning an important cornerstone, showing significant potential for fast extraction of data patterns. Online course delivery and associated tutoring are transforming into customizable, on-demand services driven by the learner. Besides automated grading, strong potential exists for the development and deployment of next generation intelligent tutoring software agents. Self-adaptive, online tutoring agents exhibiting "intelligent-like" behavior, being capable "to learn" from the learner, will become the next educational superstars. Over the past decade, computer-based tutoring agents were deployed in a variety of extended reality environments, from patient rehabilitation to psychological trauma healing. Most of these agents are driven by a set of conditional control statements and a large answers/questions pairs dataset. This article provides a brief introduction on Generation Z's addiction to digital information, highlights important efforts for the development of intelligent dialogue systems, and explains the main components and important design decisions for Intelligent Tutoring System.Comment: 4 page

Opportunities and Challenges in Neural Dialog Tutoring

Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors. Although there have been significant recent advances in neural conversational systems using large language models (LLMs) and growth in available dialog corpora, dialog tutoring has largely remained unaffected by these advances. In this paper, we rigorously analyze various generative language models on two dialog tutoring datasets for language learning using automatic and human evaluations to understand the new opportunities brought by these advances as well as the challenges we must overcome to build models that would be usable in real educational settings. We find that although current approaches can model tutoring in constrained learning scenarios when the number of concepts to be taught and possible teacher strategies are small, they perform poorly in less constrained scenarios. Our human quality evaluation shows that both models and ground-truth annotations exhibit low performance in terms of equitable tutoring, which measures learning opportunities for students and how engaging the dialog is. To understand the behavior of our models in a real tutoring setting, we conduct a user study using expert annotators and find a significantly large number of model reasoning errors in 45% of conversations. Finally, we connect our findings to outline future work.Comment: EACL 2023 (main conference, camera-ready

Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems

A major issue in Intelligent Tutoring Systems is off-task student behavior, especially performance-based gaming, where students systematically exploit tutor behavior in order to advance through a curriculum quickly and easily, with as little active thought directed at the educational content as possible. The goal of this research was to develop a passive visual indicator to deter and prevent off-task gaming behavior without active intervention, via graphical feedback to the student and teachers. Traditional active intervention approaches were also constructed for comparison purposes. Our passive graphical intervention has been well received by teachers, and results suggest that this technique is effective at reducing off-task gaming behavior

Developing Self-Regulated Learners Through an Intelligent Tutoring System

ABSTRACT Intelligent tutoring systems have been developed to help students learn independently. However, students who are poor selfregulated learners often struggle to use these systems because they lack the skills necessary to learn independently. The field of psychology has extensively studied self-regulated learning and can provide strategies to improve learning, however few of these include the use of technology. The present proposal reviews three elements of self-regulated learning (motivational beliefs, helpseeking behavior, and meta-cognitive self-monitoring) that are essential to intelligent tutoring systems. Future research is suggested, which address each element in order to develop selfregulated learning strategies in students while they are engaged in learning mathematics within an intelligent tutoring system. KEYWORDS Intelligent tutoring systems, self-regulated learning, metacognition DEFINING THE PROBLEM Intelligent tutoring systems (ITS) are designed to provide independent learning opportunities for students. Learning occurs through hints, tutoring, scaffolding and correctness feedback. A great body of research exists surrounding types and timing of feedback Zimmerman and Campillo PROPOSED SOLUTION To help develop self-regulated learners, these components must be explicitly taught. However, some aspects are seemingly more relevant than others when interacting with an ITS. Specifically motivational beliefs, help-seeking behavior, and meta-cognitive self-monitoring can all be addressed within the structures of intelligent tutoring systems. The following sections discuss each of these components by presenting relevant literature, sharing results of my previously published studies, and proposing future research components of my dissertation. Motivational Beliefs One aspect of the first phase of self-regulated learning is motivation. Students who are strong self-regulated learners have high self-efficacy. Schunk [11] defines self-efficacy as "an individual's judgment of his or her capabilities to perform given actions." A student's belief that they are capable of learning can be influenced by a growth mindset Help Seeking Behaviors Intelligent tutoring systems provide many different structures to support student learning. One such structure that I have explored is correctness-only feedback. I found that this simple support provided by an ITS during a homework assignment was found to improve student learning significantly compared to traditional paper and pencil homework that did not provide immediate feedbac

Adaptive Intelligent Tutoring System for learning Computer Theory

In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent manner according to the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned