Let's Set Up Some Subgoals: Understanding Human-Pedagogical Agent Collaborations and Their Implications for Learning and Prompt and Feedback Compliance

International audienceResearch on collaborative learning between humans and virtual pedagogical agents represents a necessary extension to recent research on the conceptual, theoretical, methodological, analytical, and educational issues behind co-and socially-shared regulated learning between humans. This study presents a novel coding framework that was developed and used to describe collaborations between learners and a pedagogical agent (PA) during a subgoal setting activity with MetaTutor, an intelligent tutoring system. Learner-PA interactions were examined across two scaffolding conditions: prompt and feedback (PF), and control. Learners' compliance to follow the PA's prompts and feedback in the PF condition were also examined. Results demonstrated that learners followed the PA's prompts and feedback to help them set more appropriate subgoals for their learning session the majority of the time. Descriptive statistics revealed that when subgoals were set collaboratively between learners and the PA, they generally lead to higher proportional learning gains when compared to less collaboratively set goals. Taken together, the results provide preliminary evidence that learners are both willing to engage in and benefit from collaborative interactions with PAs when immediate, directional feedback and the opportunity to try again are provided. Implications and future directions for extending co-and socially-shared regulated learning theories to include learner-PA interactions are proposed

Modeling Student Affective State Patterns during Self-Regulated Learning in Physics Playground

This dissertation research focuses on investigating the incidence of student self-regulated learning behavior, and examines patterns in student affective states that accompany such self-regulated behavior. This dissertation leverages prediction models of student affective states in the Physics Playground educational game platform to identify common patterns in student affective states during use of self-regulated learning behavior. In Study 1, prediction models of student affective states are developed in the context of the educational game environment Physics Playground, using affective state observations and computer log data that had already been collected as part of a larger project. The performances of student affective state prediction models generated using a combination of the computer log and observational data are then compared against those of similar prediction models generated using video data collected at the same time. In Study 2, I apply these affective state prediction models to generate predictions of student affective states on a broader set of data collected from students participants playing Physics Playground. In parallel, I define aggregated behavioral features that represent the self-observation and strategic planning components of self-regulated learning. Affective state predictions are then mapped to playground level attempts that contain these self-regulated learning behavioral features, and sequential pattern mining is applied to the affective state predictions to identify the most common patterns in student emotions. Findings from Study 1 demonstrate that both video data and interaction log data can be used to predict student affective states with significant accuracy. Since the video data is a direct measure of student emotions, it shows better performance across most affective states. However, the interaction log data can be collected natively by Physics Playground and is able to be generalized more easily to other learning environments. Findings from Study 2 suggest that self-regulatory behavior is closely associated with sustained periods of engaged concentration and .self-regulated learning behaviors are associated with transitions from negative affective states (confusion, frustration, and boredom) to the positive engaged concentration state. The results of this dissertation project demonstrate the power of measuring student affective states in real time and examining the temporal relationship to self-regulated learning behavior within an unstructured educational game platform. These results thus provide a building block for future research on the real-time assessment of student emotions and its relationship with self-regulated learning behaviors, particularly within online student-centered and self-directed learning contexts

Theoretical and Conceptual Approaches to Co-Regulation: A Theoretical Review

During the last two decades, interpersonal regulation in natural and digital learning environments has gained importance. Ever since the first conceptual and methodological precisions regarding collaborative learning were made, educational psychology has focused its interest on analyzing collective regulation of motivation, cognition, and behavior. Despite the fact that the body of research on co-regulation has grown, emerging epistemological frameworks evidence a lack of conceptual and theoretical clarity. In response to this situation, the authors propose a conceptual approach in order to address interpersonal regulation in four aspects: first, they describe three learning theories which have been used to study co-regulation. Second, the authors recommend a conceptual delimitation of terms regarding the learning theories on social regulation. Third, they highlight diffuse boundaries between theoretical approaches and terms used in the literature on co-regulation. Finally, the authors suggest some challenges the researchers in this field face

The sequence matters: A systematic literature review of using sequence analysis in Learning Analytics

Describing and analysing sequences of learner actions is becoming more popular in learning analytics. Nevertheless, the authors found a variety of definitions of what a learning sequence is, of which data is used for the analysis, and which methods are implemented, as well as of the purpose and educational interventions designed with them. In this literature review, the authors aim to generate an overview of these concepts to develop a decision framework for using sequence analysis in educational research. After analysing 44 articles, the conclusions enable us to highlight different learning tasks and educational settings where sequences are analysed, identify data mapping models for different types of sequence actions, differentiate methods based on purpose and scope, and identify possible educational interventions based on the outcomes of sequence analysis.Comment: Submitted to the Journal of Learning Analytic

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

Development of Self-Regulated Learning Skills Within Open-Ended Computer-Based Learning Environments for Science

Over the past decade, open-ended computer-based learning environments have been increasingly used to facilitate students’ learning of complex scientific topics. The non-linearity and open-endedness of these environments create learning opportunities for students, but can also impose challenges in terms of extraneous cognitive load and greater requirements for self-regulated learning (SRL). SRL is crucial for academic success in various educational settings. This dissertation explores how self-regulatory skills develop and the role of gender in the development of SRL skills in Virtual Performance Assessments (VPA), an immersive, open-ended virtual environment designed to assess middle school students’ science inquiry skills. Findings from three analyses combining educational data mining techniques with multilevel modeling indicated that students developed self-regulatory behaviors and strategies as they used VPA. For example, experience with VPA prepared students to adopt more efficient note-taking and note-reviewing strategies. Students who used VPA for the second time engaged in note-taking more frequently, noted a significantly higher quantity of unique information, used the control of variables strategy more frequently in note-taking, and reproduced more domain-specific declarative information in notes than students who used VPA for the first time, all of which have been found to be positively associated with science inquiry performance. Students also learned to exploit more available sources of information by applying learning strategies, in order to either solve inquiry problems, or to monitor and evaluate their solutions. Compared to the second-time users who focused primarily on answering the core inquiry question and selectively collected data, the first-time users’ behaviors showed the repetition and combination of exploratory actions such as talking with non-player characters and collecting data. In addition, consistent gender differences in SRL were observed in this study. Female students were more likely to take notes than male students; they took notes and reviewed notes more frequently and recorded a higher quantity of information in notes, especially information from the research kiosk. Females were also more likely to review notes or read research pages to assist them with the problem-solving and decision-making process than their male counterparts. Possibly due to the higher quantity of information recorded by female note-takers and their tendency to review notes over males, female students’ performance on science inquiry tasks improved across the course of using the two scenarios of VPA, whereas the male students’ science inquiry skills did not show improvement. Results from this dissertation study provide insights into the instructional design of personalized open-ended learning environments to facilitate self-regulated learning for both male and female students

Providing Intelligent and Adaptive Support in Concept Map-based Learning Environments

abstract: Concept maps are commonly used knowledge visualization tools and have been shown to have a positive impact on learning. The main drawbacks of concept mapping are the requirement of training, and lack of feedback support. Thus, prior research has attempted to provide support and feedback in concept mapping, such as by developing computer-based concept mapping tools, offering starting templates and navigational supports, as well as providing automated feedback. Although these approaches have achieved promising results, there are still challenges that remain to be solved. For example, there is a need to create a concept mapping system that reduces the extraneous effort of editing a concept map while encouraging more cognitively beneficial behaviors. Also, there is little understanding of the cognitive process during concept mapping. What’s more, current feedback mechanisms in concept mapping only focus on the outcome of the map, instead of the learning process. This thesis work strives to solve the fundamental research question: How to leverage computer technologies to intelligently support concept mapping to promote meaningful learning? To approach this research question, I first present an intelligent concept mapping system, MindDot, that supports concept mapping via innovative integration of two features, hyperlink navigation, and expert template. The system reduces the effort of creating and modifying concept maps while encouraging beneficial activities such as comparing related concepts and establishing relationships among them. I then present the comparative strategy metric that modes student learning by evaluating behavioral patterns and learning strategies. Lastly, I develop an adaptive feedback system that provides immediate diagnostic feedback in response to both the key learning behaviors during concept mapping and the correctness and completeness of the created maps. Empirical evaluations indicated that the integrated navigational and template support in MindDot fostered effective learning behaviors and facilitating learning achievements. The comparative strategy model was shown to be highly representative of learning characteristics such as motivation, engagement, misconceptions, and predicted learning results. The feedback tutor also demonstrated positive impacts on supporting learning and assisting the development of effective learning strategies that prepare learners for future learning. This dissertation contributes to the field of supporting concept mapping with designs of technological affordances, a process-based student model, an adaptive feedback tutor, empirical evaluations of these proposed innovations, and implications for future support in concept mapping.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Teaching Strategies Used to Promote EFL Autonomous Learning in Distance Education Undergraduate Students: An Initial Approach in the Framework of the Colombian Research Context

Este trabajo de naturaleza cualitativa se ocupa de las estrategias docentes utilizadas para promover el aprendizaje autónomo del inglés como lengua extranjera en la educación a distancia. Esto cobra sentido en el contexto colombiano donde la mayor parte de los estudiantes de educación superior son aprendices heterónomos y no alcanzan el nivel esperado como usuarios independientes de esta lengua extranjera. Este trabajo consiste en una investigación documental de las revistas colombianas sobre la investigación de la enseñanza de las lenguas extranjeras, con el propósito de recopilar estrategias de enseñanza que brinden los fundamentos para promover el aprendizaje autónomo del EFL en este contexto. Para lograr el propósito, se analizó una muestra de nueve (9) de 70 artículos de investigación contenidos en las revistas y fueron seleccionados siguiendo varios criterios de búsqueda. Durante este proceso se compilaron los datos torno a los tres principales constructos: educación a distancia en Colombia, las características del aprendizaje autónomo del inglés como lengua extranjera y las estrategias docentes aplicadas. Los hallazgos en torno al primer constructo muestran que el modelo educativo a distancia sobre el cual hay evidencia empírica se caracteriza por el componente a distancia sumado al presencial; del segundo constructo, el aprendizaje autónomo del inglés cuenta con una amplia cantidad de características psicológicas, cognitivas, metacognitivas y sociales; y del tercer constructo, la investigación-acción y casos estudios muestran que la promoción del aprendizaje autónomo requiere tener en cuenta la naturaleza y condiciones de la educación a distancia, el concepto de aprendizaje autónomo de EFL, los papeles de los instructores, la función de tutoría, el temario, materiales de auto-acceso, contenidos, herramientas, apoyo institucional, entre otros.This qualitative work deals with the teaching strategies used to promote English as a foreign language (EFL) autonomous learning in distance education. This makes sense within the Colombian context where most of the higher education students are heteronomous learners and do not reach the expected level as independent users of this foreign language. This work consists of a documentary investigation which takes data from Colombian research journals about teaching and learning foreign languages, to compile teaching strategies that provide the foundations to promote the EFL autonomous learning in undergraduate distance education students. To achieve this purpose, exploration, and analysis using a sample of nine (9) from 70 research articles according to various searching criteria were developed. During this process, data was compiled around three main constructs: distance education in Colombia, the characteristics of autonomous learning of English as a foreign language, and the teaching strategies applied. The findings around the first construct show that the Colombian distance education model used is characterized by the distance component added to the face-to-face component; about the second construct, autonomous English learning has a large number of psychological, cognitive, metacognitive, and social features; and the third construct, action-research and case studies show that promoting autonomous learning demands taking into account the nature and conditions of distance education, the concept of EFL autonomous learning, the roles of instructors, the tutoring function, the planning, and the syllabus, self-access materials, contents, tools, institutional support, among others

Adaptive and Re-adaptive Pedagogies in Higher Education: A Comparative, Longitudinal Study of Their Impact on Professional Competence Development across Diverse Curricula

This study addresses concerns that traditional, lecture-based teaching methods may not sufficiently develop the integrated competencies demanded by modern professional practice. A disconnect exists between conventional pedagogy and desired learning outcomes, prompting increased interest in innovative, student-centered instructional models tailored to competence growth. Despite this, nuanced differences in competence development across diverse university curricula remain underexplored, with research predominantly relying on students’ self-assessments. To address these gaps, this study employs longitudinal mixed-methods approaches with regard to theory triangulation and investigator triangulation to better understand how professional knowledge, skills, and dispositions evolve across varied curricula and contexts. This research emphasizes adaptive and re-adaptive teaching approaches incorporating technology, individualization, and experiential learning, which may uniquely integrate skill development with contextual conceptual learning. Specific attention is paid to professional education paths like design, media, and communications degrees, where contemporary competence models stress capabilities beyond core conceptual knowledge. Results from this study aim to guide reform efforts to optimize professional competence development across diverse academic areas

Possible approaches to personalisation at Cass Business School

This report discusses the result of an investigation into the personalisation of learning and how it could be applied in a university with primarily face-to-face instruction, and, a diverse student body