Theories of embodied learning emphasize that learning processes are grounded in bodily actions and interactions with the environment, suggesting that movements play a fundamental role in problem solving, decision making, and learning. This perspective holds particular relevance for making-based learning settings, where patterns of movement and spatial engagement can reveal strategic expertise. Prior research has examined distinctions between students who learned and did not learn, but manual coding of actions presents scalability and real-time application challenges. To address this gap, we develop a computer vision–based analysis pipeline for automated detection and characterization of hand movements during complex assembly tasks. In an exploratory study, we apply this approach to video data of students engaged in the assembly of a differential gearbox, quantifying metrics such as amount and speed of movement. Results indicate that learners show fewer right-hand movements than novices and exhibit reduced movement speed, with a progressive decline in speed as the task unfolds. Non-learners, by contrast, display more uneven hand movement speed. These findings, while preliminary, highlight measurable differences in actions of learners and non-learners, and therefore have potential implications for learning support. Specifically, the ability to computationally distinguish movement profiles can inform the design of adaptive learning interventions, providing real-time performance assessment and targeted feedback for making-based learning.

Making, grounded in theories of constructionism, offers learners the opportunity to construct their own knowledge through constructing personally meaningful artifacts, thus making them interested and committed to the process of their own learning. However, making-based learning is challenging to implement owing to the limitations of materials and well-qualified teachers. Online learning is a way for marginalized learners, who often do not have access to quality educational resources such as well-qualified teachers and learning materials, to access well-qualified teachers. With the growth of synchronous e-learning, learners now have opportunities to interact with their teachers and obtain immediate feedback, a process known to be productive for learning. In this paper, we report on a large-scale making-based online intervention whose design leverages the potential of online learning and cheap, easily accessible materials to create a science, technology, engineering and math (STEM) learning pathway for girls from marginalized communities by engaging them in STEM practices. Using evidence from observations and interviews we evaluate the effectiveness of the intervention in creating these learning pathways. We find that while the intervention increases learner interest in STEM practices, thus initiating a pathway to deepening participation, the language used in the intervention, technology, material and teacher factors can become barriers that have the potential to constrain this pathway and further marginalize the students. We discuss implications and potential solutions to these challenges.

Abstract: In this we focus on critical thinking activities conducted in an online environment and interaction markers based on the data collected from those activities. The work is based on the ENACT framework. We conducted an empirical study to understand the clusters of critical thinkers based on Performance and interactional marker values of the participants (n=37). The highlights the definitions of the interaction markers and conducts an clustering analysis of the of Critical Thinking patterns that emerged. The results show two clusters with similar critical thinking outcome performance but different action patterns. We discuss the need of further empirical evidence relating learning effect on the actions and Critical Thinking.

A wider incorporation of robots into classrooms is hampered by current technological limitations on full autonomy in social robots. Automated speech recognition, for example, a key enabler for vocal communication, is still unable to perform with sufficient accuracy. Past studies have shown that humans adjust their speech patterns to accommodate less skilled interlocutors. If such a response holds in human-robot interactions as well, we may be able to exploit it to lessen the burden on social robots and enable rich, autonomous vocal communication. In this paper we explore whether a robot’s speaking ability could have an impact on children’s speech patterns, learning, and engagement by designing an interaction where a child and a robot collaborate on a Tower of Hanoi puzzle. Sixteen children aged 7-14 completed this collaborative task partnered with a social robot that communicated with either high verbal (full sentences), low verbal (short phrases or single words), or nonverbal (sound-based utterances) vocalization. While we found no significant impact on children’s speech patterns or learning due to the robot’s method of vocalization, children in the non-verbal condition had a significantly lower perception of the robot’s intelligence along with higher rates of providing feedback and more instances of undoing its moves. This suggests that a link may exist between a robot’s perceived speaking ability and children’s confidence in that robot’s overall intelligence and capability in a collaborative task, as well as their empathy towards a peer they perceive as less skilled in the task.

ENaCT is a framework for the design and analysis of critical thinking environments based on 4E cognition perspectives. In this paper, we describe a web-based critical thinking environment designed to implementœ the ENaCT framework. When users perform a critical thinking task in the environment their interaction logs are captured. We report on a pilot study with undergraduate participants and analyse how participants used the affordances in the environment as they performed the critical thinking task. One case of task-related behaviours (high activity) is elaborated to highlight the current possibilities of the system and discuss implications for redesign.

Collaborative learning appears in a joint intellectual efforts of individuals to understand an object of knowledge collectively. In their search for understanding the problems, meanings, and solutions, learners employ different multi-modal strategies. In this work, we explore the role of force feedback in learners interaction with tangible hand-held robots. We designed a collaborative learning environment to provide embodied intuitions on linear mathematical functions combined with graphical representations and ran a first study involving 24 participants. Our analysis shows a positive learning gain for our learning activity. Moreover, to explore the link between different types of force feedback and learners' collaboration, we designed a focus group study with 12 participants. Our results suggest that the haptic communication channel affects the collaboration dynamic differently according to the nature of the learning task. We finish by proposing design insights for future exploration of haptic in collaborative learning.

Think-pair-share (TPS) is a classroom active learning strategy in which students work on activities, first individually, then in pairs and finally as the whole class. TPS allows students to express their reasoning, reflect on their understanding and obtain prompt feedback on their learning. While TPS is recommended to foster classroom engagement and learning, there is a lack of research based evidence in computer science education on the benefits of TPS for learning. In this study, we investigate the learning effectiveness of TPS in a CS1 course. We performed a quasi-experimental study and found that students who learned via TPS performed significantly better on a post-test than students who learned the same concept via lecture. We also conducted a survey and focus group interviews to understand student perceptions of learning with TPS. The majority of students agreed that TPS activities helped improve their conceptual understanding. From an instructor's point of view, TPS was useful to address the challenges of a large class, such as students tuning out or getting distracted and was easy to implement even in a large class.

Think-Pair-Share (TPS) is a classroom-based active learning strategy, in which students work on a problem posed by the instructor, first individually, then in pairs, and finally as a class-wide discussion. TPS has been recommended for its benefits of allowing students to express their reasoning, reflect on their thinking, and obtain immediate feedback on their understanding. While TPS is intended to promote student engagement, there is a need for research based evidence on the nature of this engagement. In this study, we investigate the quantity and quality of student engagement in a large CS1 class during the implementation of TPS activities. We did classroom observations of students over a period of ten weeks and thirteen TPS activities. We determined patterns of student engagement in the three phases using a real-time classroom observation protocol that we developed and validated. We found that 83% of students on average were fully or mostly engaged. Predominant behaviors displayed were writing the solution to the problem (Think), discussing with neighbor or writing (Pair), and following class discussion (Share). We triangulated results with survey data of student perceptions. We find that students report being highly engaged for 62% during Think phase and 70% during Pair phase.

In this paper, two iterative message passing algorithms based on belief propagation proposed for the decoding of any binary linear block code are compared. The innovation of these algorithms lies in the fact that the structure of the graph of the parity check matrix is adaptively modified to render it suitable to belief propagation decoding. The modification is done such that the least reliable bits received from the channel are leaves of the graph. These algorithms perform favorably when compared with existing hard and iterative soft decision decoding algorithms in terms of error rate while maintaining a complexity polynomial in block length. It was found that while these two algorithms are conceptually similar, their performance in terms of word error rate (as a function of signal-to-noise ratio), decoding time (average number of iterations) and the updated bit reliabilities are very different