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Understanding how to design and implement equity-based approaches to technology-rich learning can lead to increased and diversified participation in computing. Do-it-yourself (DIY) and maker approaches to interactive technology learning have been hailed as potential equalizers of science, technology, engineering, and math (STEM) education for underserved youth, a narrative challenged by scholarship that has shown that if not designed carefully, making can be exclusionary and hegemonic. Equity-based approaches to making have identified the crucial role of community educators to prioritize community assets and learner participation. We studied educators’ strategies and youth outcomes in four afterschool maker programs in urban recreation centers. Community educators used several equity-based strategies to engage youth that included: identifying their interests through direct conversation and indirect signaling, customizing program activities to respond to interests, and encouraging self-expression and authenticity. These strategies led to increased social connections among youth, and increased technology self-efficacy and project ownership.
Robot technologies have been introduced to computing education to engage learners. This study introduces the concept of co-creation with a robot agent into culturally-responsive computing (CRC). Co-creation with computer agents has previously focused on creating external artifacts. Our work differs by making the robot agent itself the co-created product. Through participatory design activities, we positioned adolescent girls and an agentic social robot as co-creators of the robot's identity. Taking a thematic analysis approach, we examined how girls embody the role of creator and co-creator in this space. We identified themes surrounding who has the power to make decisions, what decisions are made, and how to maintain social relationship. Our findings suggest that co-creation with robot technology is a promising implementation vehicle for realizing CRC.
Competence Assessment by Chunk Hierarchy Evaluation with Transcription-tasks (CACHET) was proposed by Cheng [14]. It analyses micro-behaviors captured during cycles of stimulus viewing and copying in order to probe chunk structures in memory. This study extends CACHET by applying it to the domain of graphs and charts. Since drawing strategies are diverse, a new interactive stimulus presentation method is introduced: Transcription with Incremental Presentation of the Stimulus (TIPS). TIPS aims to reduce strategy variations that mask the chunking signal by giving users manual element-by-element control over the display of the stimulus. The potential of TIPS, is shown by the analysis of six participants transcriptions of stimuli of different levels of familiarity and complexity that reveal clear signals of chunking. To understand how the chunk size and individual differences drive TIPS measurements, a CPM-GOMS model was constructed to formalize the cognitive process involved in stimulus comprehension and chunk creation.
Online videos are a promising medium for older adults to learn. Yet, few studies have investigated what, how, and why they learn through online videos. In this study, we investigated older adults' motivation, watching patterns, and difficulties in using online videos for learning by (1) running interviews with 13 older adults and (2) analyzing large-scale video event logs (N=41.8M) from a Korean Massive Online Open Course (MOOC) platform. Our results show that older adults (1) are motivated to learn practical topics, leading to less consumption of STEM domains than non-older adults, (2) watch videos with less interaction and watch a larger portion of a single video compared to non-older adults, and (3) face various difficulties (e.g., inconvenience arisen due to their unfamiliarity with technologies) that limit their learning through online videos. Based on the findings, we propose design guidelines for online videos and platforms targeted to support older adults' learning.
Academic advising brings numerous benefits to the mission of Higher Education Institutions. One central and challenging duty of advisors is course recommendation for term planning. This task requires both knowledge of the study programs as well as a thorough analysis of the students' unique circumstances. Limited time and a large student population make this task overwhelming. As a result, an important body of research has sought to expedite term planning via data-oriented decision-support tools. The impact of such tools on students has been extensively studied. However, the advisors’ perspective remains largely unexplored. We contribute to redressing this gap by studying how a grade prediction tool shapes academic advisors' approach to course recommendation. We found that while the advisors' usual strategies tend to prevail, their recommendations largely depend on the advisee's historical performance. That said, advisors also acknowledge the limitations of grades as a measure of academic success.
Stress reappraisal interventions try to shift students’ negative perceptions towards eustress, stress that can be beneficial, and help them perform better. However, it is less clear how to present them to users as online interventions that are brief, voluntary, and scale well in real-world contexts. We explore the design of online exam eustress interventions by generating six design factors (D1-6) that reinforce a core reappraisal message (D0), and evaluate them through: (i) user interviews (N = 20) revealing six findings (F1-6) on the importance of elaboration, layout, modality, and source of intervention content; (ii) a field experiment (N = 1283) showing a significant positive effect on exam scores (p = 0.003). Subgroup analyses indicate a significant effect for first-year but not for upper-year students, and no detectable gender differences. Our work offers insight into how students interact with online mindset interventions and design considerations for incorporating them into large courses.