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In our post-pandemic world, where the majority of higher education institutions have transitioned back to in-person classes, this paper argues that we must not return to pre-COVID teaching practices. Instead, we have the obligation and opportunity to create an educational experience and environment that better facilitates learning and instruction. This paper presents post-COVID best practices for employing technology in higher education based on an original survey and follow-up interviews of seventeen computing instructors at our institution. After a literature review, we describe four general categories of practices that enhance the post-COVID classroom: online student activities, digital instructor notes, remote classroom participation and collaboration, and a paperless classroom. For each of these categories, we provide vignettes to illustrate scope and intent. We also offer recommendations for addressing digital dishonesty, required infrastructure, institutional support, and being prepared to seamlessly return to a blended or fully remote environment in the event of another crisis. Finally, we identify additional emerging technological challenges and opportunities that require further effort. Overall, this paper emphasizes the need for a shift towards improved practices in the classroom rather than just a return to pre-pandemic norms. We believe implementing these recommendations will result in a more flexible, accessible, and robust post-COVID educational experience.

Over 60 years of technology development, transformation of educational policy and curriculum innovation in Ireland have resulted in the introduction of the Computer Science (CS) subject in Post-Primary (PP) Education. CS has always been conceived digitally and the Digital Learning Experiences (DLE) enacted through its curriculum are strongly interconnected to the opportunities and limitations offered by Digital Spaces (DS). However, key challenges have been identified for the successful implementation of CSE, from teachers' digital competencies and educational strategies in the classroom to learners' varying experiences of CSE. Through a systematic literature review of the educational policies and practices in Ireland's Educational System, this paper documents the digital evolution from the 1960s accounting for the CS curriculum. The literature review identifies key themes in how DS have been conceptualised through CSE, responding to learners' needs and teachers' skills and competencies, informed by emerging societal demands by providing evidence on the disparity between educational policy and practice for DS. Building upon the identified themes, this paper emphasises the importance of the design and implementation of DLE in DS such as the CS subject that considers historical lessons learned to respond to the uncertainties of the digital future.

Recently, researchers have proposed to leverage technology-supported data (log files) to investigate temporal and sequential patterns of interaction behaviors in learning processes. There are two major challenges to be addressed: clarifying the positioning of interaction levels and identifying the evolution of the interaction action patterns in learning processes, particularly for students with differing achievements. This paper explores the use of sequential pattern mining to address the evolution of student action patterns in Massive Private Online Courses (MPOCs) and compare these patterns between different achievement groups. The study was conducted with first-year undergraduate computer science students enrolled in a computer application course at a traditional open university in one of the Chinese provinces (N = 1375). The results showed the development of various action patterns in each phase of the course and the distinct action patterns for high-achieving and low-achieving students. The findings of study provide a new perspective for instructors and students to understand interaction patterns at the fine-grained level, and can help instructional designers develop learner-cantered courses and platforms to improve online learning.

In this paper we discuss the types of knowledge used by six middle school students as they engaged with a debugging task designed to integrate ideas from computer science, mathematics and science. Our findings show that the computational thinking practice of debugging is a rich source of opportunities to integrate these different disciplines. The analysis illustrates how the types of knowledge the students did and did not use at each step of the debugging process were related to their ability to succeed at each step. Our work contributes to theory and practice by uncovering implications for studying debugging through two refined frameworks and for designing debugging tasks to support transdisciplinary learning.

Collaborative learning has numerous benefits such as enhancing learners' critical thinking, developing social skills, and improving learning gains. While engaging in this interactive process, learners' satisfaction toward their partners plays a crucial role in defining the success of the collaboration. However, detecting learners' satisfaction during an ongoing collaboration remains challenging, and there are no automatic techniques to predict learners' satisfaction. In this paper, we propose a multimodal approach to automatically predict peer satisfaction for co-located collaboration with features extracted from 44 middle school learners' collaborative dialogues. We investigated three types of features extracted from learners' dialogues: 1) linguistic features indicating semantics and sentiment; 2) acoustic-prosodic features including energy and pitch; and 3) visual features including eye gaze, head pose, facial action units, and body pose. We then trained several regression models with each of those features to predict the peer satisfaction scores that learners received from their partners. The results revealed that head position and body location were significant indicators of peer satisfaction: lower head and body distances between partners were associated with more positive peer satisfaction. Next, we investigated the influence of multimodal feature fusion methods on peer satisfaction prediction accuracy: early fusion versus late fusion. We report the comparison results between models trained with (1) best-performing unimodal features, (2) multimodal features combined by early fusion, and (3) multimodal features combined by late fusion. This line of research reveals how multimodal features from collaborative dialogues are associated with peer satisfaction, and represents a step toward the development of real-time intelligent systems that support collaborative learning.

Few high schools offer Information Technology (IT)) as a subject in South Africa. This paper used Bourdieu's conception of cultural capital to investigate the reasons why many public schools are shunning away from including IT in their curriculum. Data were collected using a questionnaire and analysed using ANOVA, to determine whether cultural capital influences choosing IT as one of the subjects in the school curriculum. The results showed that public high school principals view IT as a subject that should be done in schools where there is medium-to-high cultural capital status. It is a challenge for schools with low cultural capital to offer IT among their subjects as it is not practiced by the community or society in which the schools exist. The paper concludes by recommending that there is room for the so-called 'low cultural capital' schools to offer IT among their subjects despite the challenges they are facing. Cultural capital does not hinder students' performance. Students from low cultural capital communities can achieve good grades even though they learn under difficult circumstances of inadequate educational resources.