Using Student Mood And Task Performance To Train Classifier Algorithms To Select Effective Coaching Strategies Within Intelligent Tutoring Systems (its)

The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System\u27s (ITS) coaching strategy based on the student\u27s mood. As a step toward this goal, this study evaluated the relationships between each student\u27s mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student\u27s performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student\u27s affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student\u27s interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student\u27s mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle\u27s (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank\u27s (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables

Empowering educators to be AI-ready

In this paper, we present the concept of AI Readiness, along with a framework for developing AI Readiness training. ‘AI Readiness’ can be framed as a contextualised way of helping people to understand AI, in particular, data-driven AI. The nature of AI Readiness training is not the same as merely learning about AI. Rather, AI Readiness recognises the diversity of the professions, workplaces and sectors for whom AI has a potential impact. For example, AI Readiness for lawyers may be based on the same principles as AI Readiness for Educators. However, the details will be contextualised differently. AI Readiness recognises that such contextualisation is not an option: it is essential due to the multiple intricacies, sensitivities and variations between different sectors and their settings, which all impact the application of AI. To embrace such contextualisation, AI Readiness needs to be an active, participatory training process and aims to empower people to be more able to leverage AI to meet their needs. The text that follows focuses on AI Readiness within the Education and Training sector and starts with a discussion of the current state of AI within education and training, and the need for AI Readiness. We then problematize the concept of AI Readiness, why AI Readiness is needed, and what it means. We expand upon the nature of AI Readiness through a discussion of the difference between human and Artificial Intelligence, before presenting a 7-step framework for helping people to become AI Ready. Finally, we use an example of AI Readiness in action within Higher Education to exemplify AI Readiness

The Cognitive-Affective-Social Theory of Learning in digital Environments (CASTLE)

For a long time, research on individuals learning in digital environments was primarily based on cognitive-oriented theories. This paper aims at providing evidence that social processes affect individual learning with digital materials. Based on these theories and empirical results, a social-processes-augmented theory is suggested: the Cognitive-Affective-Social Theory of Learning in digital Environments (CASTLE). This CASTLE postulates that social cues in digital materials activate social schemata in learners leading to enhanced (para-)social, motivational, emotional, and metacognitive processes. To substantiate this theory, socio-cognitive theories are used, which predict social influences on learning with digital materials. Besides, previous empirical findings are presented assuming that with a rising number of social cues in digital materials, the influence of social processes increases. Finally, consequences regarding the design of digital learning media are discussed

Artificial Intelligence Approaches in Student Modeling: Half Decade Review (2010-2015)

Intelligent Tutoring Systems (ITSs) are special classes of E-learning systems designed using Artificial Intelligence (AI) approaches to provide adaptive and personalized tutoring based on the individuality of students. The student model is an important component of an ITS that provides the base for this personalization. During the course of interaction between student and the ITS, the system observe student’s actions and other behavioral properties, create a quantitative representation of these student’s attributes called a student model

Improving mathematics in key stages two and three:evidence review

This document presents a review of evidence commissioned by the Education Endowment Foundation to inform the guidance document Improving Mathematics in Key Stages Two and Three (Education Endowment Foundation, 2017). There have been a number of recent narrative and systematic reviews of mathematics education examining how students learn and the implications for teaching (e.g., Anthony & Walshaw, 2009; Conway, 2005; Kilpatrick et al., 2001; Nunes et al., 2010). Although this review builds on these studies, this review has a different purpose and takes a different methodological approach to reviewing and synthesising the literature. The purpose of the review is to synthesise the best available international evidence regarding teaching mathematics to children between the ages of 9 and 14 and to address the question: what is the evidence regarding the effectiveness of different strategies for teaching mathematics? In addition to this broad research question, we were asked to address a set of more detailed topics developed by a group of teachers and related to aspects of pupil learning, pedagogy, the use of resources, the teaching of specific mathematical content, and pupil attitudes and motivation. Using these topics, we derived the 24 research questions that we address in this review. Our aim was to focus primarily on robust, causal evidence of impact, using experimental and quasi-experimental designs. However, there are a very large number of experimental studies relevant to this research question. Hence, rather than identifying and synthesising all these primary studies, we focused instead on working with existing meta-analyses and systematic reviews. This approach has the advantage that we can draw on the findings of a very extensive set of original studies that have already been screened for research quality and undergone some synthesis. Using a systematic literature search strategy, we identified 66 relevant meta-analyses, which synthesise the findings of more than 3000 original studies. However, whilst this corpus of literature is very extensive, there were nevertheless significant gaps. For example, the evidence concerning the teaching of specific mathematical content and topics was limited. In order to address gaps in the meta-analytic literature, we supplemented our main dataset with 22 systematic reviews identified through the same systematic search strategy