Design-activity-sequence: A case study and polyphonic analysis of learning in a digital design thinking workshop

In this case study, we report on the outcomes of a one-day workshop on design thinking attended by participants from the Computer-Supported Collaborative Learning conference in Philadelphia in 2017. We highlight the interactions between the workshop design, structured as a design thinking process around the design of a digital environment for design thinking, and the diverse backgrounds and interests of its participants. Data from in-workshop reflections and post-workshop interviews were analyzed using a novel set of analytical approaches, a combination the facilitators made by possible by welcoming participants as coresearchers

Connecting Learning Analytics and Problem-Based Learning – Potentials and Challenges

Learning analytics (LA) are a young but fast-growing field, which, according to some authors, holds big promises for education. Some claim that LA solutions can help measure and support constructivist classrooms and 21st century skills, thus creating a potential of making an alignment between LA and PBL principles and practices. Despite this argument, LA have not yet gained much interest among the Problem-Based Learning (PBL) practitioners and researchers and the possible connections between PBL and LA have not yet been properly explored. The purpose of this paper is, therefore, to investigate how LA can potentially be used to support and inform PBL practice. We do this by identifying central themes that remain constant across various orchestrations of PBL (collaboration, self-directed learning, and reflection) and present examples of LA tools and concepts that have been developed within LA and neighbouring fields (e.g. CSCL) in connection to those themes. This selection of LA solutions is later used as a basis for discussing wider potentials, challenges and recommendations for making connections between PBL and LA. &nbsp

Semantically annotated lesson observation data in learning analytics datasets: A reference model

Learning analytics (LA) and lesson observations are two approaches frequently used to study teaching and learning processes. In both cases, in order to extract meaningful data interpretations, there is a need for contextualization. Previous works propose to enrich LA datasets with observation data and to use the learning design as a framework to guide the data gathering and the later analysis. However, the majority of lesson observation tools collect data that is not compliant with LA datasets. Moreover, the connection between the learning design and the data gathered is not straightforward. This study reflects upon our research-based design towards an LA model for context-aware semantically annotated lesson observations that may be integrated in multimodal LA datasets. Six teachers (out of which 2 were also researchers) with previous experience in lesson observation were engaged in a focus group interview and participatory design session that helped us to evaluate the LA model through the conceptual design of Observata (a lesson observation tool that implements our model). The findings show the feasibility and usefulness of the proposal as well as the potential limitations in terms of adoption

Teaching Analytics: Towards Automatic Extraction of Orchestration Graphs Using Wearable Sensors

"Teaching analytics" is the application of learning analytics techniques to understand teaching and learning processes, and eventually enable supportive interventions. However, in the case of (often, half-improvised) teaching in face-to-face classrooms, such interventions would require first an understanding of what the teacher actually did, as the starting point for teacher reflection and inquiry. Currently, such teacher enactment characterization requires costly manual coding by researchers. This paper presents a case study exploring the potential of machine learning techniques to automatically extract teaching actions during classroom enactment, from five data sources collected using wearable sensors (eye-tracking, EEG, accelerometer, audio and video). Our results highlight the feasibility of this approach, with high levels of accuracy in determining the social plane of interaction (90%, k=0.8). The reliable detection of concrete teaching activity (e.g., explanation vs. questioning) accurately still remains challenging (67%, k=0.56), a fact that will prompt further research on multimodal features and models for teaching activity extraction, as well as the collection of a larger multimodal dataset to improve the accuracy and generalizability of these methods

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks

The use of tools of data mining to decision making in engineering education—A systematic mapping study

In recent years, there has been an increasing amount of theoretical and applied research that has focused on educational data mining. The learning analytics is a discipline that uses techniques, methods, and algorithms that allow the user to discover and extract patterns in stored educational data, with the purpose of improving the teaching‐learning process. However, there are many requirements related to the use of new technologies in teaching‐learning processes that are practically unaddressed from the learning analytics. In an analysis of the literature, the existence of a systematic revision of the application of learning analytics in the field of engineering education is not evident. The study described in this article provides researchers with an overview of the progress made to date and identifies areas in which research is missing. To this end, a systematic mapping study has been carried out, oriented toward the classification of publications that focus on the type of research and the type of contribution. The results show a trend toward case study research that is mainly directed at software and computer science engineering. Furthermore, trends in the application of learning analytics are highlighted in the topics, such as student retention or dropout prediction, analysis of academic student data, student learning assessment and student behavior analysis. Although this systematic mapping study has focused on the application of learning analytics in engineering education, some of the results can also be applied to other educational areas

Moulding student emotions through computational psychology: affective learning technologies and algorithmic governance

Recently psychology has begun to amalgamate with computer science approaches to big data analysis as a new field of ‘computational psychology’ or ‘psycho-informatics,’ as well as with new ‘psycho-policy’ approaches associated with behaviour change science, in ways that propose new ways of measuring, administering and managing individuals and populations. In particular, ‘social-emotional learning’ has become a new focus within education. Supporters of social-emotional learning foresee technical systems being employed to quantify and govern learners’ affective lives, and to modify their behaviours in the direction of ‘positive’ feelings. In this article I identify the core aspirations of computational psychology in education, along with the technical systems it proposes to enact its vision, and argue that a new form of ‘psycho-informatic power’ is emerging as a source of authority and control over education

Learning Sciences Beyond Cognition: Exploring Student Interactions in Collaborative Problem Solving

Composed of insightful essays from top figures in their respective fields, the book also shows how a thorough understanding of this critical discipline all but ensures better decision making when it comes to education

Learning Analytics for the Formative Assessment of New Media Skills

Recent theories of education have shifted learning environments towards student-centred education. Also, the advancement of technology and the need for skilled individuals in different areas have led to the introduction of new media skills. Along with new pedagogies and content, these changes require new forms of assessment. However, assessment as the core of learning has not been modified as much as other educational aspects. Hence, much attention is required to develop assessment methods based on current educational requirements. To address this gap, we have implemented two data-driven systematic literature reviews to recognize the existing state of the field in the current literature. Chapter four of this thesis focus on a literature review of automatic assessment, named learning analytics. This chapter investigates the topics and challenges in developing new learning analytics tools. Chapter five studies all assessment types, including traditional and automatic forms, in computational thinking education. Computational thinking education, which refers to the teaching of problem-solving skills, is one of the new media skills introduced in the 21st century. The findings from these two literature reviews categorize the assessment methods and identify the key topics in the literature of learning analytics and computational thinking assessment. Studying the identified topics, their relations, and related studies, we pinpoint the challenges, requirements, and opportunities of using automatic assessment in education. The findings from these studies can be used as a guideline for future studies aiming to enhance assessment methods in education. Also, the literature review strategy in this thesis can be utilized by other researchers to develop systematic data-driven literature reviews in future studies