Journal of Computing in Higher Education

Evaluation is a crucial part of the teaching and learning process in any higher education institution and one that has gone through a deep change. This has been particularly true since the Bologna Declaration (http://www.ehea.info/page-ministerial-conference-bologna-1999, 1999) ushered in the European higher education area, with the subsequent major rise in the employment of continuous assessment methods focused on student participation. This article analyses the impact on academic performance of e-continuous assessment based on e-tests on a virtual platform as a previous step towards the substitution of the traditional evaluation system, based on a final exam, with a continuous evaluation system, prescribed as an alternative preferred by the regulations of multiple Spanish universities. Microeconometric models have been applied to a database of 250 first-year students on the Business Administration and Management course at the University of Seville (Spain). Our findings show that e-tests could prevent the risk of students dropping out and could also provide a credible predictor of students’ academic marks in the theoretical contents of the subject, but not in those of a practical or applied nature. Based on the results of this evaluation, an e-continuous assessment has been developed in the subject, which has become the majority option for students, with 90% participation, while also increasing pass rates. Moreover, the positive effect of a computing environment does not appear to be limited to the classroom, but also extends to students’ home environments. This teaching experience shows that the swift feedback that e-tools provides, especially in especially in environments of large class size such as in the class evaluated, could support instructors’ personal tutoring of students’ progress and promote a greater implementation of e-continuous assessment in Spanish higher education.

Use of online video lectures is increasingly common. However, students may struggle to self-regulate their attention and passively process the content. This study examined whether, and for whom, different types of embedded learning prompts improve student learning from video lectures. Undergraduate physics students (N = 253) watched an online, asynchronous physics lecture video as part of their course content. Students were randomly assigned to receive embedded cognitive prompts, metacognitive prompts, or a no-prompt control condition during the video, then took a quiz. A subset of students also reported perceived cognitive load after the video. Overall, students who received cognitive prompts exhibited higher quiz scores than students in the control condition. Scores in the metacognitive prompt condition did not differ from those of either other condition, demonstrating a middling effect. Perceived cognitive load did not differ between conditions. A subset of students additionally completed measures of individual differences in study approaches and metacognitive skills. Students who reported having more disorganized study approaches benefited the most from cognitive prompts. Individual differences in surface/deep processing approaches and metacognitive skills did not interact with prompt condition. These findings detail a simple intervention to increase cognitive engagement during online lectures while not increasing the reported mental effort required. These prompts may be most effective for students who otherwise have difficulty organizing their study time.

This study examined and compared the effects of two types of online peer assessment, namely, “free selection and invitation” (FS&I) assessment and the commonly implemented “assigned-pair” (AP) assessment, on undergraduates’ research competencies, flow, motivation, and interaction, supported by the Cloud Classroom online learning system. Ninety-three undergraduates from a research methods course participated in this study. They were randomly divided into two groups: an FS&I group and an AP group. The two groups experienced exactly the same teaching conditions except for different online peer assessment types. The study was conducted over one semester (16 weeks). Both quantitative and qualitative methods were used to examine the effects of the two types of online peer assessment. Regarding research competencies that were represented by conceptual knowledge and research proposal, this study found that the FS&I and AP groups showed no significant difference in conceptual knowledge score, and that the FS&I group performed significantly better in the quality of research proposals than the AP group. The FS&I group also manifested higher levels of flow and motivation than the AP group. Additionally, social network analysis (SNA) revealed that the FS&I group exhibited more interactions and closer connections with peers than the AP group. These results suggest that FS&I online peer assessment is an effective scaffolding that can improve undergraduates’ research competencies, learning engagement and willingness to interact. The implications of this study are also discussed.

COVID-19 dramatically influenced students’ and staff’s learning and teaching experiences and approaches to learning. While many papers examined individual experiences in the context of higher education, synthesising these papers to determine enabling and hindering influences of digital adaptation was needed to guide the next phase of online learning reforms. This study explored the main dimensions of digital technology adaptation in higher education during the COVID-19 pandemic. The consequences for student and staff experiences and what aspects should be sustained and developed were discussed in this review. A total of 90 articles (published between 1st January 2020 and 30th June 2021) were identified and analysed based on the preferred reporting items for systematic reviews and meta-analyses framework. Four dimensions (with associated sub-factors) were found to influence student and staff experiences: techno-economic; personal and psychological; teaching, learning and assessment; and social. The findings highlighted that an integrated approach, across institutional, technical platforms, and individuals would be required to sustain digital learning initiatives during the crisis time.

HyFlex learning environments have been meeting the unique needs of students and institutions for nearly 20 years. However, it was the pandemic that gave HyFlex its widespread acceptance and application. Literature suggests that HyFlex may now be considered part of the new norm in education and therefore, further study is needed on how it affects both teaching and learning. Our flipped design thinking course leverages active learning requiring the instructor and students to interact extensively. We piloted a specific version of HyFlex we named “Interactive Synchronous HyFlex” where students can participate on a daily basis in person or synchronously online. In this specific instance of HyFlex we explore: (1) Does student academic performance differ in the HyFlex environment compared to the Face to Face only environment? And (2) Does student academic performance differ based on how they chose to participate in the HyFlex course? Data were collected for this quasi-experimental study design on overall semester grades and three significant design projects during the semester. We compared the course offered as a Face to Face only experience to the course offered as a HyFlex course enabling remote participation. Second, we parse students in the HyFlex course into two categories: those who did not participate remotely vs. those who participated remotely once or more times. Students in the HyFlex course had a significantly different grade distribution earning more A’s and more F’s than their Face to Face only counterparts. Given the positive results of the Interactive Synchronous HyFlex approach, we plan to continue implementing it in our introductory design course though we will increase our attention on the remote students as they may need additional scaffolding to be successful.

Cheating in online exams without face-to-face proctoring has been a general concern for academic instructors during the crisis caused by COVID-19. The main goal of this work is to evaluate the cost of these dishonest practices by comparing the academic performance of webcam-proctored students and their unproctored peers in an online gradable test. With this aim in mind, we carried out a randomized field experiment using a simple video surveillance system through Google Meet during an online closed-book final exam of an Introduction to Microeconomics course. Given that all conditions except for webcam monitoring were identical, differences in between-subjects scores are attributed to academic dishonesty. After controlling for potential confounding factors, including gender, academic degree, instructor, previous score and whether students were repeaters or not, we found that those students who were proctored via webcam obtained statistically significant lower scores in the final exam than those who were not using this surveillance system with a low level of invasiveness. Inspection of the potential factors behind these differences in scores suggests that the poorer performance of proctored students is more related to academic dishonesty than to reasons involving anxiety or heterogeneity factors.

Institutions of higher education are interested in supporting the academic success of their students in the most effective ways possible. Increasingly, this means engaging students in a blend of both online and in-person learning activities as well as connecting students with supportive communities in both modalities. This research explores the efforts of an institution that is trying to create positive post-secondary learning experiences for thousands of adults who have experienced significant barriers to accessing higher education through traditional routes. Students around the world are supported through both online and local, in-person communities created to support their academic engagement. In this study we used social network analysis to examine and compare the influence of both in-person and online support communities on student affective and cognitive engagement. We also compared the strength of engagement support from peers, teachers, and personal family/friend networks. We found significant differences in the strength, frequency, and type of support offered by the online and in-person aspects of the program as well as in the students’ self-reported engagement and enjoyment between the two modalities. While in-person communities in this context had the greatest impact on both blended and in-person student engagement, the online community also had a significant effect on student engagement. This research adds to the blended learning literature by illustrating that course communities are not only distinguished based on time and actors, but also by modality. Support communities that occur in blended and online contexts affect student engagement in different, and often complementary, ways.

Sentiment Analysis (SA), a technique based on applying artificial intelligence to analyze textual data in natural language, can help to characterize interactions between students and teachers and improve learning through timely, personalized feedback, but its use in education is still scarce. This systematic literature review explores how SA has been applied for learning assessment in online and hybrid learning contexts in higher education. Findings from this review show that there is a growing field of research on SA, although most of the papers are written from a technical perspective and published in journals related to digital technologies. Even though there are solutions involving different SA techniques that can help predicting learning performance, enhancing feedback and giving teachers visual tools, its educational applications and usability are still limited. The analysis evidence that the inclusion of variables that can affect participants’ different sentiment expression, such as gender or cultural context, remains understudied and should need to be considered in future developments.

The aim of this study is to examine the development of self-regulated learning (SRL) of university students in an EFL course via Flipped classroom through a novel measurement roadmap. Microanalytic techniques were used throughout the process rather than students’ perceptions at a given moment. SRL skills scores are reported as well as sample testimonies from students as qualitative data used to explain the analytic measures. The study was conducted with 12 students studying at a public university and lasted for 6 weeks. A new system FCMWEB is designed throughout to monitor online SRL development taking Zimmerman (2008) SRL model as a basis. While the forethought phase was monitored in the online sessions and the performance and self-reflection phase were associated with the face-to-face sessions. Records from FCMWEB as microanalytic, worksheet, observation notes and self-reflection form were used as data collection tools. During the study, the sub skills of forethought phase, self-reflection phase and performance phase are analyzed and validated through students’ perspectives. The developments in the skills of the forethought and self-reflection phases were between sufficient and advanced levels. The performance phase was mostly sufficient, but also included poor and advanced levels. The results provide implications for course designers and instructors who desire to provide a better flipped classroom experience in EFL for university students.

This paper offers a synthesis on computing identity and the experiences of women and minoritized populations in computing and computer science in higher education. Examining computing identity and its role in the computing experiences of minoritized undergraduates can help us better understand ways to engage and support women and minoritized individuals in the computing field thus increasing participation and persistence of these groups in computing. In this article, we present a review of research literature on computing identity within the experiences of women and minoritized undergraduate students in computing. The research questions guiding this work are: (1) What research literature exists about computing identity?, (2) According to existing literature, what are the experiences of women and racially minoritized undergraduates in computing (e.g., persons who identify as Black, Hispanic/Latinx, Indigenous people)?, and (3) What theoretical frameworks are used to guide existing computing identity literature? Emergent themes include the need to focus on exposure to computing, persistence, career expectations, and engagement. While there is no consensus on a computer identity framework, there is research guided by social cognitive career theory, social identity theory, and practice theory. We conclude that computing identity, including its multiple conceptualizations, offers multiple opportunities for informing and broadening participation of minoritized populations in computing. Future research should include further conceptual exploration of computing identity.

The empirical study investigates what log files and process mining can contribute to promoting successful learning. We want to show how monitoring and evaluation of learning processes can be implemented in the educational life by analyzing log files and navigation behavior. Thus, we questioned to what extent log file analyses and process mining can predict learning outcomes. This work aims to provide support for learners and instructors regarding efficient learning with computer-based learning environments (CBLEs). We evaluated log file and questionnaire data from students (N = 58) who used a CBLE for two weeks. Results show a significant learning increase after studying with the CBLE with a very high effect size (p < .001, g = 1.71). A cluster analysis revealed two groups with significantly different learning outcomes accompanied by different navigation patterns. The time spent on learning-relevant pages and the interactivity with a CBLE are meaningful indicators for Recall and Transfer performance. Our results show that navigation behaviors indicate both beneficial and detrimental learning processes. Moreover, we could demonstrate that navigation behaviors impact the learning outcome. We present an easy-to-use approach for learners as well as instructors to promote successful learning by tracking the duration spent in a CBLE and the interactivity.

In recent years, embodied learning has gained currency in the field of education, allowing interactivity between users, thus contributing to collaborative learning in the flow of embodied immersive technology. Evidence based studies conducted in this field tackled the effectiveness of this approach on students’ performance and learning outcomes in children’s education. However, scarcity of scoping reviews treating the outcome of using embodied collaborative learning on higher education students’ performance was detected. Considering the relevance of building a theoretical background guiding further research in this current field, this meta-analysis was conducted to fill the gap. This article describes the current issues on research about embodied collaborative learning in STEM education in XR learning settings to identify advances and gaps. Published papers between 2013 and 2023 were selected from educational databases, identifying 12 documents related to the subject of interest of this meta-analysis. Results show that embodied learning have positive impact on social interactivity and collaboration between students. Positive impact on students’ academic outcomes was also detected. Alongside with the preceding, embodied collaborative learning has a positive impact on overall students’ social flow (motivation, cohesion, emotions, interaffectivity, satisfaction). This article presents a theoretical background for embodied collaborative testing tools based on embodied social presence and collaboration practical criteria.

Institutions of higher education possess large amounts of learning-related data in their student registers and learning management systems (LMS). This data can be mined to gain insights into study paths, study styles and possible bottlenecks on the study paths. In this study, we focused on creating a predictive model for study completion time estimation. Additionally, we explored the data to find out what features may affect the rapid completion of studies for a bachelor’s degree in an institution of higher education. We combined data from two sources: the Moodle LMS and a student register. The study exploited data from the entire study duration of the students. The data we extracted from the Moodle LMS focuses on the student’s diligence in respecting assignment deadlines. Based on the data, we created a model for predicting study duration and achieved an accuracy of 72%. According to this study, among the factors that may be influenced by the student herself, we found out that the most important predictors for fast study completion are a study pace that is more intensive at the end of studies, submitting assignments well before deadline and having a considerable amount of the grade 4.

This study investigates the role of learning analytics in enhancing the learning experience within Massive Open Online Courses (MOOCs) through a two-phase design-based research approach, focusing on a Social Work MOOC. Initial engagement analysis revealed strong interactions with course content, especially with introductory elements and reflection quizzes, underscoring their importance in sustaining learner commitment. The subsequent empirical design refinement identified two primary learner clusters: Comprehensive Sequential Engagers and Interactive Early Engagers. The Comprehensive Sequential Engagers demonstrate a methodical approach, starting later and favoring a structured knowledge acquisition process, suggesting the need for adaptable course structures and early checkpoints to track progress. Conversely, the Interactive Early Engagers engage early and actively, driven by curiosity and a preference for exploratory learning, indicating a need for flexible content navigation and personalized learning pathways. These findings highlight that learning analytics can significantly inform MOOC design, providing valuable insights into tailoring educational experiences to meet diverse learner needs and behaviors. Despite these benefits, challenges remain in integrating learning analytics into course design, including obtaining timely and accurate data, ensuring data literacy among educators, and addressing cultural resistance to data-driven approaches. This study calls for further research to expand the adoption of learning analytics, examine the barriers to its integration, and improve its scalability across different educational contexts.

This study had two objectives: (1) to evaluate the validity of an instrument for measuring differentiated cognitive loads in its Spanish version; and (2) to evaluate the three types of cognitive loads and their relationship with self-efficacy, self-concept, and interest in programming of students in an introductory course. Understanding and assessing cognitive loads when learning computer programming is key to supporting student learning. While there are instruments in English and German assessing the different types of cognitive loads, there is no validated instrument in Spanish. This study took place during the implementation of an online training program in basic programming, with a sample of 1162 students. We used Exploratory Factor Analysis and Confirmatory Factor Analysis to validate the structure of the instrument. The results allowed us to establish a factorial structure of the subjective scale of differentiated cognitive loads, managing to measure the germane, intrinsic, and extraneous cognitive loads. The bivariate correlation analysis allowed identifying statistically significant associations between the study variables, including (a) the negative relationship between extraneous cognitive load and germane cognitive load and (b) the negative relationship between extraneous cognitive load and self-efficacy in programming.

Pervasive internet use in higher education has rendered social anxiety a lesser problem in circumventing interactions. However, the attenuation of vitality in interactions still remains a contentious issue, especially in asynchronous online discussions. This study aims to elucidate how anonymity and social presence affect the relationship between avoidance of interaction and peer and content interaction in online discussions. In the true experimental study, we recruited 123 first-year university students, of which 62 were randomly assigned to the anonymous group, and 61 to the identified group. This paper adopts a moderated mediation model, in which anonymity and social presence are included as moderator and mediator, respectively. We revealed that the “straightforward” relationship between avoidance of interaction and peer and content interaction is actually highly complex. The results substantiate a full mediation of social presence and moderation of anonymity in favor of disclosed identity. This study accentuates the critical role of social presence in fostering interactions. The results also suggest that disclosing discussants’ identities might be useful during asynchronous interactions in online discussions for increasing social presence only if their avoidance of interaction is low; in the case of a high avoidance of interaction, anonymity should be preferred.

With technological advances, institutional stakeholders are considering evidence-based developments such as Curriculum Analytics (CA) to reflect on curriculum and its impact on student learning, dropouts, program quality, and overall educational effectiveness. However, little is known about the CA state of the art in Higher Education Institutions (HEIs), where dropout is a worldwide problem. Through a systematic literature review, this study summarizes 59 manuscripts about CA published until April 2024. The aim of this review is to identify: 1) WHERE CA was used; 2) WHO were the CA target stakeholders; 3) WHY CA was proposed; 4) WHAT types of data and what types of data gathering and analysis methods are employed; 5) HOW CA was designed, implemented and evaluated and what the stakeholders' role was; and 6) WHICH limitations and constraints affect CA and WHICH recommended practices could contribute to the CA success. Results from our review reveal a considerable number of CA solutions available. However, there is a need for more evidence on how CA solutions inform decision-making among various stakeholders. Thus, more longitudinal studies are needed, involving stakeholders not only in the design but also in the implementation and evaluation of CA solutions. At the same time, findings suggest that including multiple data sources can enrich the analysis and enable triangulation. Additionally, the lack of evidence on the role of CA in dropouts and decision-making in higher education institutions requires more future research on this aspect. Finally, researchers, practitioners, and decision-makers can use the findings obtained in this review to inform future research and practices on how to leverage CA to improve student learning, enhance the learning experience, and reduce student dropouts.

The study investigates how the 2×2 configuration of homogeneous and heterogeneous distributions of extraversion and prior knowledge influences group outcomes, including satisfaction, performance, and stability. Based on the standard deviation of extraversion and prior knowledge, groups were established to test experimentally, what form of grouping leads to best outcomes. The randomized controlled trial took place in the context of an online course with 355 prospective students, working in 82 groups. The two characteristics extraversion and prior knowledge were distributed algorithmically, either homogeneously or heterogeneously. Results showed no superiority of heterogeneous formation, yet there were systematic interaction effects by the experimental group formation on satisfaction and performance. Due to the increasing relevance of online groupwork, explorative results are reported and integrated. Ideas for future research on group formation as an important influencing factor are discussed. Findings supports knowledge about cooperative online learning by optimizing the selection of group members using a therefore implemented algorithm.