Supporting STEM knowledge and skills in engineering education – PELARS project

In this paper we present our proposal for improving education with hands-on, project-based and experimental scenarios for engineering students with the use of learning analytics. We accomplish this through teacher and learner engagement, user studies and evaluated trials, performed at UCV (University of Craiova, Romania) and DTU (Technical University of Denmark). The PELARS project (Practice-based Experiential Learning Analytics Research And Support) provides technological tools and ICT-based methods for collecting activity data (moving image-based and embedded sensing) for learning analytics (data-mining and reasoning) of practice-based and experiential STEM. This data is used to create analytics support tools for teachers, learners and administrators, providing frameworks for evidence-based curriculum design and learning systems. The PELARS project creates behavioral recording inputs, proving a new learning analytic that is scalable in application, and bridge qualitative and quantitative methods through reasoning and feedback from input data. The project serves to better understand learners' knowledge in physical activities in laboratory and workshop environments, as well as informal learning scenarios. PELARS traces and helps assess learner progress through technology enhancement, in novel ways building upon current research. The project results in learning analytics tools for practice-based STEM learning that are appropriate for real-world learning environments

Physics Problem Solving Skills with IBL-STEMWeb: Students on Small Islands in Maluku

Learning on small islands in Maluku still has many obstacles. It is limited by inadequate supporting facilities and infrastructure in the form of internet availability in accessing learning materials. Maluku's natural resources, both biological and non-biological, are very abundant. However, they have not been managed properly in improving the community's welfare. Therefore, education is one of the spearheads in improving the quality of students so that they can manage the existing nature well in the future. One of the recommended lessons is IBL-STEWeb to improve the academic quality of students. The use of IBL-STEWeb can take into account the involvement of students' preconceptions, provide in-depth basic knowledge, help students make connections within the context of a conceptual framework, and can map students' knowledge in a way that facilitates the search and application of information

Temporal pathways to learning: how learning emerges in an open-ended collaborative activity

The learning process depends on the nature of the learning environment, particularly in the case of open-ended learning environments, where the learning process is considered to be non-linear. In this paper, we report on the findings of employing a multimodal Hidden Markov Model (HMM) based methodology to investigate the temporal learning processes of two types of learners that have learning gains and a type that does not have learning gains in an open-ended collaborative learning activity. Considering log data, speech behavior, affective states and gaze patterns, we find that all learners start from a similar state of non-productivity, but once out of it they are unlikely to fall back into that state, especially in the case of the learners that have learning gains. Those who have learning gains shift between two problem solving strategies, each characterized by both exploratory and reflective actions, as well as demonstrate speech and gaze patterns associated with these strategies, that differ from those who don't have learning gains. Further, the teams that have learning gains also differ between themselves in the manner in which they employ the problem solving strategies over the interaction, as well as in the manner they express negative emotions while exhibiting a particular strategy. These outcomes contribute to understanding the multiple pathways of learning in an open-ended collaborative learning environment, and provide actionable insights for designing effective interventions

Capturing and analyzing verbal and physical collaborative learning interactions at an enriched interactive tabletop

Interactive tabletops can be used to provide new ways to support face-to-face collaborative learning. A little explored and somewhat hidden potential of these devices is that they can be used to enhance teachers' awareness of students' progress by exploiting captured traces of interaction. These data can make key aspects of collaboration visible and can highlight possible problems. In this paper, we explored the potential of an enriched tabletop to automatically and unobtrusively capture data from collaborative interactions. By analyzing that data, there was the potential to discover trends in students' activity. These can help researchers, and eventually teachers, to become aware of the strategies followed by groups. We explored whether it was possible to differentiate groups, in terms of the extent of collaboration, by identifying the interwoven patterns of students' speech and their physical actions on the interactive surface. The analysis was validated on a sample of 60 students, working in triads in a concept mapping learning activity. The contribution of this paper is an approach for analyzing students' interactions around an enriched interactive tabletop that is validated through an empirical study that shows its operationalization to extract frequent patterns of collaborative activity. © 2013 International Society of the Learning Sciences, Inc. and Springer Science+Business Media New York

Exploring the nature of students’ collaborative interactions during a hands-on ill-structured engineering design task

Engineering education is experiencing a shift in curriculum format toward more emphasis on collaborative design work. This can be accomplished through means such as collaborative ill-structured tasks, which provide students with experience authentic to industry. However, research on effective ill-structured task design in the context of undergraduate group problem solving is relatively limited. Studies have explored how to design and construct ill-structured tasks that effectively engage students and promote higher learning outcomes and group collaboration, but these tasks have primarily been limited to two-dimensional representations that lack opportunity for students to realize their design implications in the physical world. While some tasks may include three-dimensional representation of task content, little is known about the influence on students’ collaborative interaction that can result from the use of physical, hands-on task products in this context. This study seeks to address this gap by characterizing the nature of students’ interactions as they worked in small groups on an ill-structured engineering design task for which a physical object was a central component. The study uses mixed methods to analyze the interactions and experiences of twenty undergraduate engineering students in five groups as they worked together to dissect a product, model its components, and make justified design changes to their model. Ethnographic observations were recorded during multiple dissection sessions for each of the five groups. Thematic analysis was used to identify initial trends in the data and to develop a coding scheme, which was then applied to characterize participants’ behaviors and collaborative processes at both individual and group levels. Frequencies of codes were compared against task scores to investigate the impact of participation in identified behaviors and processes on group performance. Results indicated positive relationships between 1) participation in dissection and task scores, and 2) participation in collaborative reflection and task scores, both of which are meaningful for future collaborative task design. The study supports the evolution of collaborative engineering problem solving by contributing to our understanding of the impact of hands-on learning in design tasks

Analysing, visualising and supporting collaborative learning using interactive tabletops

The key contribution of this thesis is a novel approach to design, implement and evaluate the conceptual and technological infrastructure that captures student’s activity at interactive tabletops and analyses these data through Interaction Data Analytics techniques to provide support to teachers by enhancing their awareness of student’s collaboration. To achieve the above, this thesis presents a series of carefully designed user studies to understand how to capture, analyse and distil indicators of collaborative learning. We perform this in three steps: the exploration of the feasibility of the approach, the construction of a novel solution and the execution of the conceptual proposal, both under controlled conditions and in the wild. A total of eight datasets were analysed for the studies that are described in this thesis. This work pioneered in a number of areas including the application of data mining techniques to study collaboration at the tabletop, a plug-in solution to add user-identification to a regular tabletop using a depth sensor and the first multi-tabletop classroom used to run authentic collaborative activities associated with the curricula. In summary, while the mechanisms, interfaces and studies presented in this thesis were mostly explored in the context of interactive tabletops, the findings are likely to be relevant to other forms of groupware and learning scenarios that can be implemented in real classrooms. Through the mechanisms, the studies conducted and our conceptual framework this thesis provides an important research foundation for the ways in which interactive tabletops, along with data mining and visualisation techniques, can be used to provide support to improve teacher’s understanding about student’s collaboration and learning in small groups

Increasing Passersby Engagement with Public Large Interactive Surfaces

Despite the proliferation of Public Large Interactive Surfaces (PLISs), and their potential to provide a more engaging and interactive user experience, these surfaces often go unnoticed by passersby, or not immediately comprehensible in terms of usage. Current research in addressing this problem involves modeling the user-surface interaction through observational studies, and deriving recommendations for interface design to facilitate the interaction. This approach is often context-specific, requires elaborate setup, and lacks experimental control. To mitigate this problem, an interaction model, named DISCOVER, was developed by drawing ideas from classic usability research and focusing on the discoverability aspect of the interaction. This approach allows the model to serve as a lens for understanding and synthesizing existing work on PLISs, and to be used as an evaluation framework to assess effectiveness of potential designs. To accompany this evaluation capability, a laboratory-based evaluation methodology was developed to allow researchers to quickly implement and evaluate potential designs, particularly for the early stages of interaction that precede the more commonly studied explicit and direct interaction (e.g., touches, mid-air gestures). Using the model and the evaluation methodology, a proximity-based interaction mechanism using animated content and shadow visualizations was designed and evaluated as an effective technique in drawing attention from unknowing study participants. A follow-up, more conventional in-the-wild study also verified this finding, and further demonstrated the usefulness of shadow visualizations in drawing attention from passersby, retaining them, and enticing playful interaction. The goal of this thesis is to better equip researchers and practitioners of PLISs with tools that allow them to evaluate and improve existing interfaces, and to provide them with insights into designing future ones employing better and more engaging technologies