Journal of Computing in Higher Education

While extensive studies on informal online learning have been well documented to afford teachers’ collaborative learning and knowledge sharing, little is still known about their motivational factors regarding the continuance intention of informal online learning. To this end, an extended expectation confirmation model (ECM) was proposed including intrinsic and extrinsic motivation. The proposed research model and several hypotheses were empirically evaluated using questionnaire surveys with the valid data collected from 231 Chinese in-service teachers in the shared mobile learning community. The results consolidate the appropriateness of the extended ECM to explain teachers’ informal online learning continuance. Specifically, satisfaction is the major determinant of continuance intention, followed by perceived usefulness and intrinsic motivation. In addition, extrinsic motivation positively predicts perceived usefulness and confirmation. The results of this study provide some theoretical and practical implications into in-service teachers’ continuance intention of informal online learning.

This research explores preservice classroom teachers’ perceived importance, self-efficacy beliefs, participation frequencies and conceptions related to new literacies. The research is framed using a dual-level theory of new literacies. The participants of the study consisted of 364 preservice teachers studying in the department of primary education at two state universities in Turkey. The findings revealed that (i) while preservice teachers displayed high participation in new literacies requiring the consumption of information, they displayed low participation in information production activities; (ii) the importance preservice teachers attached to online or digital literacy activities and their self-efficacy beliefs significantly predicted their participation in these activities; (iii) preservice teachers’ conceptual understandings with regard to new literacies were compatible with their participation frequencies and perceived importance with regard to online or digital literacy activities. Some suggestions for preservice teachers’ skills, strategies, dispositions and practices related to new literacies are discussed.

Digital competence is considered to be a crucial learning outcome in education in the 21st century. In this context, research highlights the fact that the perceptions that instructors manifest about different aspects of Information and Communication Technologies (ICTs) condition these professionals’ behaviour towards these learning resources. In the same line, this study aims to analyse the effects that exist between a series of dimensions related to the perception of university teaching staff on the capacity of ICTs to respond to different needs of students, on perceived efficacy and attitudes towards these tools and, lastly, on active behaviour towards their use. To do so, 345 university instructors from the Spanish educational system filled in an online questionnaire. The application of a Structural Equation Model underscores the fact that the ability of ICTs to respond to the different needs of students in the university classroom and their perceived efficacy in the teaching–learning process both exert a positive effect on attitudes in favour of their incorporation into the classroom. In turn, these attitudes also have a significant effect on active behaviour with ICT resources. In addition, various mediating effects are seen to influence an active behaviour. All this gives rise to a discussion on the implications of these results to encourage the training of university teaching staff in the knowledge and management of ICTs. Increasing confidence in ICTs as effective tools to respond to different needs could significantly favour positive attitudes and behaviour so that these resources are actively integrated into the classroom.

Innovative, pedagogically informed instructional design is instrumental in increasing student engagement and improving learning outcomes in online learning environments. Interactive learning resources provide students with the opportunity to engage with content in a more personalised manner. H5P (HTML 5 Package) is a collaborative platform that allows developers to create interactive content and has been regularly used in education settings. Some evidence suggests using interactive H5P resources in online education courses could lead to greater student engagement. However, to date, there has been little investigation into whether H5P resources can improve student learning outcomes. The current study aimed to assess whether using interactive H5P resources improved assessed learning outcomes in an online undergraduate psychology course. A randomized cross-over design was utilized to test whether students exposed to H5P interactive videos had improved assessment results when compared to a control group. This study found no meaningful differences in assessment scores between students exposed to H5P versus those that were not. There was low overall engagement with the interactive content. However, students who did engage with the resources reported a positive experience and indicated a preference for more interactive elements in future courses. Future research should extend on the instructional design obstacles identified in this study, for example, by examining whether improved accessibility and education on the benefits of interactive resources would increase engagement and grades.

Blended Learning (BL) as a pedagogical approach has increased in significance during the COVID-19 pandemic, with blended and online learning environments becoming the new digital norm for higher educational institutions around the globe. While BL has been discussed in the literature for thirty years, a common approach has been to categorise learner cohorts to support educators in better understanding students’ relationships with learning technologies. This approach, largely unsupported by empirical evidence, has failed to adequately address the challenges of integrating learning technologies to fit with non-traditional students’ preferences, their BL self-efficacy and the associated pedagogical implications. Focusing on student preference, our study presents findings from a pre-COVID survey of undergraduate students across four campuses of an Australian regional university where students shared their learning technology preferences and the self-regulated learning that influenced their academic self-efficacy in a BL context. Findings show students want consistency, relevance, and effectiveness with the use of BL tools, with a preference for lecture recordings and video resources to support their learning, while email and Facebook Messenger were preferred for communicating with peers and academic staff. Our study suggests a quality BL environment facilitates self-regulated learning using fit-for-purpose technological applications. Academic self-efficacy for BL can increase when students perceive the educational technologies used by their institution are sufficient for their learning needs.

Computer programming is a skill of increasing importance in scientific and technological fields. However, in introductory computer science (CS1) courses in higher education, approximately one in every three students fails. A common reason is that students are overwhelmed by an accelerated and inflexible pace of learning that jeopardizes success. Accordingly, in the computer science education literature it has been suggested that the pedagogical philosophy of ‘mastery learning,’ which supports students progressing at their own pace, can improve academic outcomes of CS1 courses. Nevertheless, few extended mastery learning implementations in CS1 have been documented in the literature, and there is a lack of guidance and best practices to foster its adoption. In this paper, we present a four-year action research study in which a modular mastery-based CS1 course was designed, evaluated and improved in successive iterations with cohorts of engineering freshmen in a Latin American research university (N = 959). In the first year of the intervention, only 19.3% of students passed the course in their first semester attempting it. In successive iterations, the instructional design, teaching and learning activities, course content, and course management were iteratively improved such that by the fourth year of offering 77.1% of students passed the course in their first semester. Over this period, course attrition was reduced from 25.0% to 3.8% of the cohort, and students’ mean time spent in the course decreased from 23.2 weeks (SD = 7.38) to 14.9 (SD = 3.64). Results indicate that modularization for mastery learning is a viable approach for improving academic results in a CS1 course. Practical considerations towards successful implementation of this approach are presented and discussed.

Adult learners are a significant proportion of distance learners and many of these students are working mothers. Several instructional design models center the learner, and this requires understanding the learner needs, strengths, and context. There is a gap in the literature describing the experience of modern working mother students in distance education. To understand this experience, the researchers interviewed and observed six academically high-achieving working mother students as they participated in their distance education courses during the pandemic. A discourse analysis approach was utilized to analyze the data. This extreme sample revealed several strategies that these students used to be successful despite their challenges. The findings suggest that understanding the experiences of distance learners as they study in the home are important for effective course design. More specifically, working mothers face significant distractions in their study environments, but the cognitive load can be reduced by making use of their prior knowledge, scaffolding instruction, and encouraging social presence. Additional strategies from the literature that address these constructs are provided for instructors and instructional designers.

As online learning has become an inevitable trend in the post-peak era of the COVID-19 pandemic, distributed pair programming (DPP) is gaining momentum in both education and industry. DDP serves as a collaborative programming approach and also benefits the development of computational thinking, a fundamental skill in today’s world. This study conducted a systematic review of studies on DPP published after 2010 to understand the themes and factors that impact the team effectiveness of DPP and thus inform future research and practices on how to better leverage this approach for teaching and learning. The results showed that individual characteristics attracted major investigations in the selected 23 studies, including prior programming experience, actual skill, perceived skill, gender, personality, time management, confidence, and self-esteem, with pair compatibility identified as a critical team design factor that significantly affects programmers’ satisfaction. Although the feel-good factor in the team process was investigated, no significant impact was found. Under the team environment theme, we compared different opinions on the orientation (e.g., scripted roles) and the use of technology (e.g., integrated development environment tools). Future research should investigate how task structure influences team effectiveness of DPP and relates to computational thinking education. Additionally, because most studies were conducted in higher education contexts, more research in primary and secondary educational contexts is also needed.

With the rapid adoption of online learning across higher education, there is an urgent need to identify its challenges and ways of addressing them. Online group projects, in particular, present significant issues for educators. This paper presents the findings of a systematic literature review identifying the key challenges of online group projects, together with strategies to address them. From a corpus of 114 recent papers, the 57 most relevant were analysed, to identify themes related to challenges and strategies. Key challenges were: low and uneven participation by students; a lack of clarity and preparation for students; and poor relationships. Strategies for addressing challenges were: careful design of projects, particularly regarding fair assessment; clear guidance and preparation of students; and practical and emotional support throughout, to encourage confidence and engagement. The findings of this review will enable educators to design and facilitate online group projects which students find rewarding and valuable.

Earning Open Badges instead of regular grades and credits can be a motivating factor for high-performing students in terms of attending classes and completing assignments in extracurricular courses, but to what extent? And for what student profiles? To tackle these questions, we conducted a quantitative study with high-performing students. Each student involved in the study had consecutively attended two Java programming courses—one where credits and regular grades were issued for their achievements and performance in the course, and another extracurricular one where Open Badges were issued instead. The study compared the achievement goal orientation (AGO) of each student in the two courses (Wilcoxon paired test). It also examined how students’ AGO scores in the Open-Badges-only course were associated with class attendance, completion of assignments and public display of their achievements (badges)—both as individual correlations with these variables (Spearman method), as well as associations with student profiles based on these variables (identified with Ward hierarchical clustering). The results indicate that high-performing students feel less motivated in terms of outperforming/under-performing others and have less fear of not learning enough if they receive Open Badges rather than regular grades. Also, a small portion of high-performers will be fully engaged in an Open-Badges-only course (attendance/completing assignments), while the majority will attend but complete a few assignments or just attend. Still, their AGO is not correlated with attending classes, completing assignments and displaying badges.

Research in gamified learning is still needed to expound how gamification may be employed to realistically yield positive effects on learning motivation. It is essential to evaluate whether gamification can foster autonomous forms of motivation, such as intrinsic motivation, which has been related to learning persistence and performance quality, instead of non-autonomous extrinsic motivation, that has been shown to be unsustainable or at worse, harmful to learning. To explore this, the current study investigated how gamified learning affected various motivational dimensions such as the sense of competence, autonomy, valuation, intrinsic motivation, identified regulation and external regulation to provide a clearer picture of the motivational influence of gamification. This study involved a learning activity that was gamified in a long-term context. The findings showed that gamifying a non-graded learning activity increased the sense of autonomy and intrinsic motivation. Interestingly, the findings showed that gamification reduced non-autonomous motivation in an examination-oriented course. Nevertheless, gamified learning did not yield significant effects on the sense of competence and valuation. The relationships between the various motivational dimensions were discussed in this paper.

The purpose of this study is to explore the influence of the microlearning instructional approach in an online introductory database programming classroom. The ultimate goal of this study is to inform educators and instructional designers on the design and development of microlearning content that maximizes student learning. Grounded within the frameworks of cognitive theories of learning—Cognitive load theory, Cognitive theory of multimedia learning, and Cognitive, affective theory of learning with media, this exploratory case study collected student assessment data from ten-course topic quizzes and exam scores in midterm and final exams. Additionally, the students' and the course instructor's perceptions while experiencing the microlearning instructional approach were collected and analyzed. As a result, the students scored significantly higher in the course topic quizzes under microlearning instruction compared to the video lectures condition. The students experienced less cognitive load and were more motivated and engaged throughout the learning process under the microlearning instruction. The students also reported some challenges of microlearning instruction, including missing social aspects of learning, fragmented learning for some complex topics, and Canvas LMS platform issues. Overall, this inquiry suggests the potential of microlearning as an instructional approach for introductory programming concepts. Some key implications of the findings are also addressed.

This study investigated the effects of pre-training types on cognitive load, self-efficacy, and problem-solving in computer programming. Pre-training was provided to help learners acquire schemas related to problem-solving strategies. 84 undergraduate students were randomly assigned to one of three groups and each group received three different types of pre-training: 1) WOE (worked-out example) and metacognitive scaffolding, 2) faded WOE and metacognitive scaffolding, and 3) WOE and faded metacognitive scaffolding. After the pre-training phase, the participants’ cognitive load, self-efficacy, and programming problem-solving skills were analyzed. Then, during the training phase, the participants were asked to attempt a programming problem-solving task with faded WOE and faded metacognitive scaffoldings. After the training phase, the participants’ cognitive load, self-efficacy, and programming problem-solving were analyzed again. The findings revealed that providing both cognitive scaffolding (i.e., WOE or faded WOE) and non-faded metacognitive scaffolding during the pre-training phase is effective for novice learners for optimizing cognitive load, promoting self-efficacy, and enhancing programming problem-solving skills.

The purpose of this multiple-case study was to investigate how expert instructors in case-based learning (CBL) conceptualize, structure, facilitate, and assess asynchronous online discussions while addressing challenges. Accordingly, we first interviewed three expert instructors with extensive teaching experience using CBL in higher education in online learning environments and then observed their online courses. Results indicated that (a) how instructors conceptualize case discussions may relate to different conceptualizations of their instructor role, (b) instructors typically clustered facilitation strategies creating posts that included content expertise, social congruence, and cognitive congruence, but how these strategies were implemented differed across instructors, and (c) instructors differentiated solutions to challenges associated with observed difficulties on the instructor and student side. Implications for practice and research are provided.

Laboratory activities are central to undergraduate student learning in science and engineering. With advancements in computer technology, many laboratory activities have shifted from providing students experiments in a physical mode to providing them in a virtual mode. Further, physical and virtual modes can be combined to address a single topic, as the modes have complementary affordances. In this paper, we report on the design and implementation of a physical and virtual laboratory on the topic of jar testing, a common process for drinking water treatment. The assignment for each laboratory mode was designed to leverage the mode’s affordances. Correspondingly, we hypothesized each would elicit a different subset of engineering epistemic practices. In a naturalistic, qualitative study design based on laboratory mode (physical or virtual) and laboratory order (virtual first or physical first), we collected process, product, and reflection data of students’ laboratory activity. Taking an orientation that learning is participation in valued disciplinary practice, data were coded and used to characterize how students engaged with each laboratory mode. Results showed that the virtual laboratory elicited more conceptual epistemic practices and the physical laboratory more material epistemic practices, aligning with the affordances of each mode. When students completed the laboratory in the virtual mode first, students demonstrated greater engagement in epistemic practices and more positive perceptions of their learning experience in the virtual mode than when they completed the physical mode first. In contrast, engagement in the physical mode was mostly unaffected by the laboratory order.

Coding think-aloud transcripts is time-consuming and labor-intensive. In this study, we examined the feasibility of predicting students’ reasoning activities based on their think-aloud transcripts by leveraging the affordances of text mining and machine learning techniques. We collected the think-aloud data of 34 medical students as they diagnosed virtual patients in an intelligent tutoring system. The think-aloud data were transcribed and segmented into 2,792 meaningful units. We used a text mining tool to analyze the linguistic features of think-aloud segments. Meanwhile, we manually coded the think-aloud segments using a medical reasoning coding scheme. We then trained eight types of supervised machine learning algorithms to predict reasoning activities based on the linguistic features of students’ think-aloud transcripts. We further investigated if the performance of prediction models differed between high and low performers. The results suggested that students’ reasoning activities could be predicted relatively accurately by the linguistic features of their think-aloud transcripts. Moreover, training the predictive models using the data instances of either high or low performers did not lower the models’ performance. This study has significant methodological and practical implications regarding the automatic analysis of think-aloud protocols and real-time assessment of students’ reasoning activities.

Learning management systems (LMS) have emerged as a standard component of higher education institutions for the web-based delivery and management of courses. The COVID-19 pandemic highlighted the value of LMS in facilitating online teaching and learning. However, the significance of examining the factors that impact LMS use success during the pandemic has been underestimated. Moreover, despite previous attempts to evaluate students’ LMS experience, most research failed to connect the actual use of LMS to students’ learning success. To address these gaps, we developed and validated an empirical and theory-based instrument measuring students’ LMS experience. The choice of constructs was informed by a scoping review of LMS measures and interviews with a representative sample of students and teachers about their LMS use. By adding constructs that are relevant to learning in the LMS, the current study provided a more comprehensive measurement that captures students’ learning experience in the platform. We provided evidence for the measurement invariance of the scales with their Chinese translation as well. By addressing the limitations and building on this study’s findings, researchers can further advance our understanding of LMS experiences and contribute to developing more effective e-learning systems to support teaching and learning in higher education.

Research in computer-supported collaborative learning has explored various ways to support learner-learner interaction as healthcare professionals engage in online formats. While studies have explored various socio-emotional learning outcomes, learners’ psychological safety has yet to be explored as healthcare professionals engage in collaborative problem-solving. To address this gap, the qualitative study employed semi-structured interviews to understand occupational therapy students’ (N = 10) perceptions of psychological safety as they engaged in an online learning class. The resulting themes of this study described the feelings associated with different forms of interactions requiring psychological safety: (a) being vulnerable, (b) fear of being misunderstood, (c) need to protect/protection, and (d) group cohesion. The findings have implications for online learner-learner interactions and computer-supported collaborative learning. For example, learners discussed how the perceived permanence of online learning lead to a sense of self-preservation and reticence to discuss the ill-structured and potentially controversial nature of complex problems. Additional aspects of psychological safety in online learning highlighted the importance of shared experiences, learning from failure, and community building.

As learning analytics use grows across U.S. colleges and universities, so does the need to discuss the plans, purposes, and paths for the data collected via learning analytics. More specifically, students, faculty, and others who are impacted by learning analytics use should have more information about their campus’ learning analytics practices than many colleges and universities currently provide. Therefore, in the current text, the authors leverage networked learning to create a networked learning analytics logic model that supports colleges and universities in developing more transparent, ethical, inclusive learning analytics plans. The authors build on their previous learning analytics framework as well as extant learning analytics literature to develop the networked learning analytics logic model. The model offers flexibility that allows for adaptive implementation by institutions that are both new to or already engaging in learning analytics initiatives. We encourage those considering learning analytics to implement the model and disseminate their findings so that the model can evolve to align with the dynamic nature of learning analytics implementations.

This study aims to track college students’ on-task rate during the teaching process and to analyze the influence of instructional strategies on on-task rate through the aspects of observable and internal engagement indicators. Thirty-six undergraduate students at a higher education institution in China participated in the study. Students’ behaviors and their EEG signals were recorded from fifty-one learning activities. Analyses have been focused on identifying the determinants of student’s engagement levels and revealing the impacts of behavioral sequences and cognitive sequences on student’s engagement levels. The results show that (1) instructional strategies, classroom behaviors, and cognitive states were significant predictors of students’ on-task rate; (2) the continuity of classroom behaviors improved the on-task rate; and (3) the standard deviations of attention and cognitive load were positively correlated with the on-task rate. This study describes a case of integrating multimodal data analysis in classroom teaching and discusses practical implications for improving classroom teaching.