Real-Time Affective Support to Promote Learner’s Engagement

abstract: Research has shown that the learning processes can be enriched and enhanced with the presence of affective interventions. The goal of this dissertation was to design, implement, and evaluate an affective agent that provides affective support in real-time in order to enrich the student’s learning experience and performance by inducing and/or maintaining a productive learning path. This work combined research and best practices from affective computing, intelligent tutoring systems, and educational technology to address the design and implementation of an affective agent and corresponding pedagogical interventions. It included the incorporation of the affective agent into an Exploratory Learning Environment (ELE) adapted for this research. A gendered, three-dimensional, animated, human-like character accompanied by text- and speech-based dialogue visually represented the proposed affective agent. The agent’s pedagogical interventions considered inputs from the ELE (interface, model building, and performance events) and from the user (emotional and cognitive events). The user’s emotional events captured by biometric sensors and processed by a decision-level fusion algorithm for a multimodal system in combination with the events from the ELE informed the production-rule-based behavior engine to define and trigger pedagogical interventions. The pedagogical interventions were focused on affective dimensions and occurred in the form of affective dialogue prompts and animations. An experiment was conducted to assess the impact of the affective agent, Hope, on the student’s learning experience and performance. In terms of the student’s learning experience, the effect of the agent was analyzed in four components: perception of the instructional material, perception of the usefulness of the agent, ELE usability, and the affective responses from the agent triggered by the student’s affective states. Additionally, in terms of the student’s performance, the effect of the agent was analyzed in five components: tasks completed, time spent solving a task, planning time while solving a task, usage of the provided help, and attempts to successfully complete a task. The findings from the experiment did not provide the anticipated results related to the effect of the agent; however, the results provided insights to improve diverse components in the design of affective agents as well as for the design of the behavior engines and algorithms to detect, represent, and handle affective information.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Modes and Mechanisms of Game-like Interventions in Intelligent Tutoring Systems

While games can be an innovative and a highly promising approach to education, creating effective educational games is a challenge. It requires effectively integrating educational content with game attributes and aligning cognitive and affective outcomes, which can be in conflict with each other. Intelligent Tutoring Systems (ITS), on the other hand, have proven to be effective learning environments that are conducive to strong learning outcomes. Direct comparisons between tutoring systems and educational games have found digital tutors to be more effective at producing learning gains. However, tutoring systems have had difficulties in maintaining students€™ interest and engagement for long periods of time, which limits their ability to generate learning in the long-term. Given the complementary benefits of games and digital tutors, there has been considerable effort to combine these two fields. This dissertation undertakes and analyzes three different ways of integrating Intelligent Tutoring Systems and digital games. We created three game-like systems with cognition, metacognition and affect as their primary target and mode of intervention. Monkey\u27s Revenge is a game-like math tutor that offers cognitive tutoring in a game-like environment. The Learning Dashboard is a game-like metacognitive support tool for students using Mathspring, an ITS. Mosaic comprises a series of mini-math games that pop-up within Mathspring to enhance students\u27 affect. The methodology consisted of multiple randomized controlled studies ran to evaluate each of these three interventions, attempting to understand their effect on students€™ performance, affect and perception of the intervention and the system that embeds it. Further, we used causal modeling to further explore mechanisms of action, the inter-relationships between student€™s incoming characteristics and predispositions, their mechanisms of interaction with the tutor, and the ultimate learning outcomes and perceptions of the learning experience

Carelessness and Affect in an Intelligent Tutoring System for Mathematics

We investigate the relationship between students’ affect and their frequency of careless errors while using an Intelligent Tutoring System for middle school mathematics. A student is said to have committed a careless error when the student’s answer is wrong despite knowing the skill required to provide the correct answer. We operationalize the probability that an error is careless through the use of an automated detector, developed using educational data mining, which infers the probability that an error involves carelessness rather than not knowing the relevant skill. This detector is then applied to log data produced by high-school students in the Philippines using a Cognitive Tutor for scatterplots. We study the relationship between carelessness and affect, triangulating between the detector of carelessness and field observations of affect. Surprisingly, we find that carelessness is common among students who frequently experience engaged concentration. This finding implies that a highly engaged student may paradoxically become overconfident or impulsive, leading to more careless errors. In contrast, students displaying confusion or boredom make fewer careless errors. Further analysis over time suggests that confused and bored students have lower learning overall. Thus, their mistakes appear to stem from a genuine lack of knowledge rather than carelessness

INTELLIGENT TUTORING SYSTEMS, PEDAGOGICAL AGENT DESIGN, AND HISPANIC ENGLISH LANGUAGE LEARNERS

According to the most recent data from the National Center of Education Statistics (NCES) there were approximately 5 million English Language Learners (ELLs) in the U.S. public schools in the Fall of 2016, representing about 10% of the student population (2019). Spanish is the primary language for most ELL students, by a large margin. As a group, ELLs have faced a deeply rooted and persistent math achievement gap (U.S. Department of Education, 2015). Despite research indicating that intelligent tutors and animated pedagogical agents enhance learning, many tutors are not designed with ELLs in mind. As a result, Hispanic ELL students may experience difficulty accessing the relevant content when using a tutor. This mixed-method research investigates how a tutor can reach Hispanic ELL students, based on the social and cultural Identity framework of the Figured Worlds Theory by Holland et al., (1998). Students will socially and culturally engage with their animated pedagogical agents constructing figured worlds of learning and connection that have the power to shape the students’ senses of themselves as learners of math. This study investigates how Hispanic ELL students perceive the utility of and relate to a learning companion (LC) design. Data was examined from 76 middle school students interacting with a math tutor, MathSpring. The findings indicate that ELL students find the MathSpring LC more useful and helpful than do non-ELL students and the ELL students designed LCs that looked more like themselves than did the non-ELL students. The findings also indicate that students formed ‘She/Me Connection’ and ‘She is Like Me’ figured worlds

Effective Tutoring with Empathic Embodied Conversational Agents

This thesis examines the prospect of using empathy in an Embodied Tutoring System (ETS) that guides students through an online quiz (by providing feedback on student answers and responding to self-reported student emotion). The ETS seeks to imitate human behaviours successfully used in one-to-one human tutorial interactions. The main hypothesis is that the interaction with an empathic ETS results in greater learning gains than a neutral ETS, primarily by encouraging positive and reducing negative student emotions using empathic feedback. In a preparatory study we investigated different strategies for expressing emotion by the ETS. We established that a multimodal strategy achieves the best results regarding how accurately human participants can recognise the emotions. This approach was used in developing the feedback strategy for our empathic ETS. The preparatory study was followed by two studies in which we compared a neutral with an empathic ETS. The ETS in the second of these studies was developed using results from the first of these studies. In both studies, we found no statistically significant difference in learning gains between the neutral and empathic ETS. However, we did discover a number of interactions between the ETS system, learning gains and, in particular 1) student scores on an empathic tendency test and 2) student ability. We also analysed the subjective responses and the relation between self-reported emotions during the quiz and student learning gains. Based on our studies in a formal class room setting, we assess the prospects of using empathic agents in a classroom setting and describe a number of requirements for their effective use

Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion

Social media (SM) have become an integral part of our lives, expanding our inter-linking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand however, some serious negative implications of SM have repeatedly been highlighted in recent years, pointing at various SM threats for society, and its teenagers in particular: from common issues (e.g. digital addiction and polarization) and manipulative influences of algorithms to teenager-specific issues (e.g. body stereotyping). The full impact of current SM platform design -- both at an individual and societal level -- asks for a comprehensive evaluation and conceptual improvement. We extend measures of Collective Well-Being (CWB) to SM communities. As users' relationships and interactions are a central component of CWB, education is crucial to improve CWB. We thus propose a framework based on an adaptive "social media virtual companion" for educating and supporting the entire students' community to interact with SM. The virtual companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term, by balancing the level of SM threat the students are exposed to, as well as in the long term, by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. This framework offers an initial step on understanding how to design SM systems and embedded educational interventions that favor a more healthy and positive society

Modeling Tutoring Knowledge

This is a preliminary version of the chapter, the final one can be accessed at http://www.springerlink.com/content/978-3-642-14362-5#section=784256&page=1&locus=29This chapter introduces the topic "modeling tutoring knowledge" in ITS research. Starting with its origin and with a characterization of tutoring, it proposes a general definition of tutoring, and a description of tutoring functions, variables, and interactions. The Interaction Hypothesis is presented and discussed, followed by the development of the tutorial component of ITSs, and their evaluation. New challenges are described, such as integrating the emotional states of the learner. Perspectives of opening the Tutoring Model and of equipping it with social intelligence are also presented

Integrating Socially Assistive Robots into Language Tutoring Systems. A Computational Model for Scaffolding Young Children's Foreign Language Learning

Schodde T. Integrating Socially Assistive Robots into Language Tutoring Systems. A Computational Model for Scaffolding Young Children's Foreign Language Learning. Bielefeld: Universität Bielefeld; 2019.Language education is a global and important issue nowadays, especially for young children since their later educational success build on it. But learning a language is a complex task that is known to work best in a social interaction and, thus, personalized sessions tailored to the individual knowledge and needs of each child are needed to allow for teachers to optimally support them. However, this is often costly regarding time and personnel resources, which is one reasons why research of the past decades investigated the benefits of Intelligent Tutoring Systems (ITSs). But although ITSs can help out to provide individualized one-on-one tutoring interactions, they often lack of social support. This dissertation provides new insights on how a Socially Assistive Robot (SAR) can be employed as a part of an ITS, building a so-called "Socially Assistive Robot Tutoring System" (SARTS), to provide social support as well as to personalize and scaffold foreign language learning for young children in the age of 4-6 years. As basis for the SARTS a novel approach called A-BKT is presented, which allows to autonomously adapt the tutoring interaction to the children's individual knowledge and needs. The corresponding evaluation studies show that the A-BKT model can significantly increase student's learning gains and maintain a higher engagement during the tutoring interaction. This is partly due to the models ability to simulate the influences of potential actions on all dimensions of the learning interaction, i.e., the children's learning progress (cognitive learning), affective state, engagement (affective learning) and believed knowledge acquisition (perceived learning). This is particularly important since all dimensions are strongly interconnected and influence each other, for example, a low engagement can cause bad learning results although the learner is already quite proficient. However, this also yields the necessity to not only focus on the learner's cognitive learning but to equally support all dimensions with appropriate scaffolding actions. Therefore an extensive literature review, observational video recordings and expert interviews were conducted to find appropriate actions applicable for a SARTS to support each learning dimension. The subsequent evaluation study confirms that the developed scaffolding techniques are able to support young children’s learning process either by re-engaging them or by providing transparency to support their perception of the learning process and to reduce uncertainty. Finally, based on educated guesses derived from the previous studies, all identified strategies are integrated into the A-BKT model. The resulting model called ProTM is evaluated by simulating different learner types, which highlight its ability to autonomously adapt the tutoring interactions based on the learner's answers and provided dis-engagement cues. Summarized, this dissertation yields new insights into the field of SARTS to provide personalized foreign language learning interactions for young children, while also rising new important questions to be studied in the future