Journal of Computing in Higher Education最新文献

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.

This article reports research into calculative and optionalized forms of online/blended course design in higher education. This was investigated through a critical case study, centered on two faculty members and one instructional designer at a university in the United States, and using an interpretive framework that highlighted the effects of calculation and optionalization in education. The course design practices at the designer’s disposal tended to distort the teaching ideals towards which the faculty members aimed, along with many of the teaching approaches they relied on to achieve their goals. The faculty often felt restricted in their ability to form relationships with their students, while also observing that students tended to resist their attempts to engage in what they referred to as formational activities. Through these and other experiential tensions, the faculty left the project with a pervasive sense of ambiguity about course design and its contribution towards their experience as educators. The article concludes by exploring what implications these findings have for the study and practice of online/blended course design in higher education.

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.