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This journal aims to: provide a vehicle for scholarly presentation and exchange of information between professionals, researchers and practitioners in the technology-enhanced education field; contribute to the advancement of scientific knowledge regarding the use of technology and computers in higher education; and inform readers about the latest developments in the application of information technologies (ITs) in higher education learning, training, research and management.

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The Globethics.net Library contains vol. 13(2016) to current.

Pengunggahan Barusan

  • The impact of a virtual teaching assistant (chatbot) on students' learning in Ghanaian higher education

    Harry Barton Essel; Dimitrios Vlachopoulos; Akosua Tachie-Menson; Esi Eduafua Johnson; Papa Kwame Baah (SpringerOpen, 2022-11-01)
    Abstract Chatbot usage is evolving rapidly in various fields, including higher education. The present study’s purpose is to discuss the effect of a virtual teaching assistant (chatbot) that automatically responds to a student’s question. A pretest–posttest design was implemented, with the 68 participating undergraduate students being randomly allocated to scenarios representing a 2 × 2 design (experimental and control cohorts). Data was garnered utilizing an academic achievement test and focus groups, which allowed more in depth analysis of the students’ experience with the chatbot. The results of the study demonstrated that the students who interacted with the chatbot performed better academically comparing to those who interacted with the course instructor. Besides, the focus group data garnered from the experimental cohort illustrated that they were confident about the chatbot’s integration into the course. The present study essentially focused on the learning of the experimental cohort and their view regarding interaction with the chatbot. This study contributes the emerging artificial intelligence (AI) chatbot literature to improve student academic performance. To our knowledge, this is the first study in Ghana to integrate a chatbot to engage undergraduate students. This study provides critical information on the use and development of virtual teaching assistants using a zero-coding technique, which is the most suitable approach for organizations with limited financial and human resources.
  • Reflexive pedagogy at the heart of educational digital transformation in Latin American higher education institutions

    Ana Carolina Useche; Álvaro H. Galvis; Frida Díaz-Barriga Arceo; Alberto Elí Patiño Rivera; Claudia Muñoz-Reyes (SpringerOpen, 2022-10-01)
    Abstract This paper makes a critical review of educational and operational issues related with pandemic and postpandemic lessons in Latin American higher education institutions (LATAM HEI), as background for uncovering key elements to innovate educational practices in technology-mediated higher education. The authors adapted the reflexive pedagogy framework to conduct in depth analysis of innovation experiences mediated with educational technologies and draw conclusions for curricular and digital transformation of LATAM HEI.
  • Shifting online during COVID-19: A systematic review of teaching and learning strategies and their outcomes

    Joyce Hwee Ling Koh; Ben Kei Daniel (SpringerOpen, 2022-11-01)
    Abstract This systematic literature review of 36 peer-reviewed empirical articles outlines eight strategies used by higher education lecturers and students to maintain educational continuity during the COVID-19 pandemic since January 2020. The findings show that students’ online access and positive coping strategies could not eradicate their infrastructure and home environment challenges. Lecturers’ learning access equity strategies made learning resources available asynchronously, but having access did not imply that students could effectively self-direct learning. Lecturers designed classroom replication, online practical skills training, online assessment integrity, and student engagement strategies to boost online learning quality, but students who used ineffective online participation strategies had poor engagement. These findings indicate that lecturers and students need to develop more dexterity for adapting and manoeuvring their online strategies across different online teaching and learning modalities. How these online competencies could be developed in higher education are discussed.
  • Are flipped classrooms less stressful and more successful? An experimental study on college students

    Betul Aydin; Veysel DEMIRER (SpringerOpen, 2022-11-01)
    Abstract The flipped classroom model, which is a technology-supported model that employs active learning strategies, has been studied many times. However, the effect of the model on psychological variables has not been adequately questioned. In this context, this study aims to investigate the effects of flipped classroom model on the students’ assignment stress and academic achievement. For this purpose, a quasi-experimental study was designed; the pre- and post-test control group model was used. The study was conducted with the participation of 44 undergraduate pre-service teachers for 11-week period in Material Design and Use in Education course. Students' assignment stress was measured with a scale, while their academic achievement was evaluated by considering course success and material development scores. Also, students’ opinions were investigated in the process. The experimental group students followed the courses outside the class through interactive videos, and they completed the given assignments in-class with the group collaboration. On the other hand, the control group students followed the lessons in-class (face-to-face), and they completed the given assignments outside of the class with the group collaboration. Consequently, it was found that the assignment stress of the students in the experimental group decreased more than the students in the control group. In addition, the course success of the students in the experimental group increased more than the students in the control group. However, there was no significant difference between the material development scores of groups. Finally, a significant portion of the students’ who experienced the flipped classroom model, reported positive opinions about the model.
  • Correction: What do we want to know about MOOCs? Results from a machine learning approach to a systematic literature mapping review

    Ignacio Despujol; Linda Castañeda; Victoria I. Marín; Carlos Turró (SpringerOpen, 2022-10-01)
  • Compared to what? Effects of social and temporal comparison standards of feedback in an e-learning context

    Marc P. Janson; Jan Siebert; Oliver Dickhäuser (SpringerOpen, 2022-10-01)
    Abstract Performance evaluation is based on comparison standards. Results can either be contrasted to former results (temporal comparison) or results of others (social comparison). Existing literature analyzed potential effects of teachers’ stable preferences for comparison standards on students’ learning outcomes. The present experiments investigated effects of learners’ own preferences for comparison standards on learning persistence and performance. Based on research and findings on person-environment-fit, we postulated a fit hypothesis for learners’ preferences for comparison standards and framed feedback on learning persistence and performance. We tested our hypotheses in two separate experiments (N = 203 and N = 132) using different manipulations of framed feedback (temporal vs. social) in an e-learning environment, thus establishing high ecological validity and allowing objective data to be collected. We found first evidence for beneficial effects of receiving framed feedback towards own preferences on learning persistence and performance in our experiments. We tested fluency as a possible underlying psychological mechanism in our second experiment and observed a larger fit effect on learning persistence under disfluency. The results are discussed regarding a new theoretical perspective on the concept of preferences for comparison standards as well as opportunities for adaptive e-learning.
  • What do we want to know about MOOCs? Results from a machine learning approach to a systematic literature mapping review

    Ignacio DESPUJOL; Linda Castañeda; Victoria I. Marín; Carlos Turró (SpringerOpen, 2022-10-01)
    Abstract By the end of 2020, over 16,300 Massive Open Online Courses (MOOCs) from 950 universities worldwide had enrolled over 180 million students. Interest in MOOCs has been matched by significant research on the topic, including a considerable number of reviews. This study uses Machine Learning techniques and human expert supervision to generate a comprehensive systematic literature mapping review that overcomes some limitations of the traditional ones and provides a broader overview of the content and main topics studied in the specialized literature devoted to MOOCs. The sample consisted of 6320 publications automatically classified within six research topics, denominated by human experts: institutional approach, pedagogical approach, evaluation, analytics, participation, and educational resources. The content analysis of the topics identified was conducted using visual network analysis, which supported the identification of different thematic sub-clusters and endorsed the classification. Results from the review show that the lowest production of MOOC papers is within the topics of the pedagogical approach and educational resources. In contrast, participation and evaluation are the most frequent ones. In addition, the most cited papers are on the topics of analytics and resources, being the pedagogical approach and the institutional approach the less cited. This highlights the need for more MOOC research from a pedagogical perspective and calls upon the presence of educators.
  • How university teachers navigate social networking sites in a fully online space: provisional views from a developing nation

    Jessie S. Barrot; Denson R. Acomular (SpringerOpen, 2022-09-01)
    Abstract Although social networking sites (SNS) have been widely investigated, very limited information is available about how teachers navigate them within a fully online learning space, the challenges they confront, and the strategies they use to overcome them. Thus, we examined these underexplored areas by interviewing 14 higher education teachers in the field of social sciences. Using a cross-case analysis, overall data indicates that teachers had varied reasons for and considered different factors when adopting SNS for online teaching. Our study also reveals that they used SNS affordances depending on their own teaching contexts and took different roles when teaching online via this platform. Although teachers generally viewed SNS as an instructional approach, they also reported several technical, pedagogical, and learner-related challenges, which they attempted to confront using a variety of strategies. These findings confirmed that teachers’ pedagogical practices and decisions in an SNS-mediated learning environment are shaped by the interaction between and among the teacher-related factors, SNS as an instructional tool, and teaching goals mediated by the policies (existing or not) and their peers. Some key implications of our findings are on designing teacher development programs, recalibrating national, institutional, and classroom policies, and implementing a systemic approach to mitigating pedagogical challenges in an online learning space. Implications for future studies are also discussed.
  • Understand group interaction and cognitive state in online collaborative problem solving: leveraging brain-to-brain synchrony data

    Xu Du; Lizhao Zhang; Jui-Long Hung; Hao Li; Hengtao Tang; Yiqian Xie (SpringerOpen, 2022-10-01)
    Abstract The purpose of this study aimed to analyze the process of online collaborative problem solving (CPS) via brain-to-brain synchrony (BS) at the problem-understanding and problem-solving stages. Aiming to obtain additional insights than traditional approaches (survey and observation), BS refers to the synchronization of brain activity between two or more people, as an indicator of interpersonal interaction or common attention. Thirty-six undergraduate students participated. Results indicate the problem-understanding stage showed a higher level of BS than the problem-solving stage. Moreover, the level of BS at the problem-solving stage was significantly correlated with task performance. Groups with all high CPS skill students had the highest level of BS, while some of the mixed groups could achieve the same level of BS. BS is an effective indicator of CPS to group performance and individual interaction. Implications for the online CPS design and possible supports for the process of online CPS activity are also discussed.
  • Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rule-based models

    Balqis Albreiki (SpringerOpen, 2022-09-01)
    Abstract Higher education institutions often struggle with increased dropout rates, academic underachievement, and delayed graduations. One way in which these challenges can potentially be addressed is by better leveraging the student data stored in institutional databases and online learning platforms to predict students’ academic performance early using advanced computational techniques. Several research efforts have focused on developing systems that can predict student performance. However, there is a need for a solution that can predict student performance and identify the factors that directly influence it. This paper aims to develop a model that accurately identifies students who are at risk of low performance, while also delineating the factors that contribute to this phenomenon. The model employs explainable machine learning (ML) techniques to delineate the factors that are associated with low performance and integrates rule-based model risk flags with the developed prediction system to improve the accuracy of performance predictions. This helps low-performing students to improve their academic metrics by implementing remedial actions that address the factors of concern. The model suggests proper remedial actions by mapping the students’ performance in each identified checkpoint with the course learning outcomes (CLOs) and topics taught in the course. The list of possible actions is mapped to this checkpoint. The developed model can accurately distinguish students at risk (total grade $$< 70\%$$ < 70 % ) from students with good performance. The Area under the ROC Curve (AUC ROC) of binary classification model fed with four checkpoints reached 1.0. Proposed framework may aid the student to perform better, increase the institution’s effectiveness and improve their reputations and rankings.
  • Developing an online learner satisfaction framework in higher education through a systematic review of research

    Florence Martin; Doris U. Bolliger (SpringerOpen, 2022-09-01)
    Abstract Satisfaction is a critical aspect of student success in online education. In this systematic review, we examine 98 articles which studied various aspects of online learner satisfaction. We specifically analyzed publication patterns, context, research methodology, research instruments, and research themes and factors pertaining to online learner satisfaction research. Among these 98 studies, the journal Internet and Higher Education published the highest number of articles (n = 8), and the majority of studies were conducted in the United States (n = 37). Thirty five percent of the studies were conducted with undergraduate students. The majority of the studies (89%) was quantitative, 68% were descriptive, and 94% used surveys. Learner characteristics was the most examined theme, followed by engagement and course delivery. Program quality, assessment, and learner support were some of the themes that were least studied. In 46 studies researchers adopted or modified existing items or instruments to measure student satisfaction. The framework benefits both online learning practitioners and researchers.
  • A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed?

    Yuanyuan Hu; Claire Donald; Nasser Giacaman (SpringerOpen, 2022-09-01)
    Abstract Automatic analysis of the myriad discussion messages in large online courses can support effective educator-learner interaction at scale. Robust classifiers are an essential foundation for the use of automatic analysis of cognitive presence in practice. This study reports on the application of a revised machine learning approach, which was originally developed from traditional, small-scale, for-credit, online courses, to automatically identify the phases of cognitive presence in the discussions from a Philosophy Massive Open Online Course (MOOC). The classifier performed slightly better on the MOOC discussions than similar previous studies have found. A new set of indicators to identify cognitive presence was revealed in the MOOC discussions, unlike those in the traditional courses. This study also cross-validated the classifier using MOOC discussion data from three other disciplines: Medicine, Education, and Humanities. Our results suggest that the cognitive classifier trained using MOOC data in only one discipline cannot yet be applied to other disciplines with sufficient accuracy.
  • VR-based health and safety training in various high-risk engineering industries: a literature review

    Ryo Toyoda; Fernando Russo-Abegão; Jarka Glassey (SpringerOpen, 2022-08-01)
    Abstract This article provides a critical review of the current studies in VR-based health and safety training, assessment techniques, training evaluation, and its potential to improve the training evaluation outcomes in various high-risk engineering industries. The results of this analysis indicate the breadth of VR-based applications in training users on a combination of topics including risk assessment, machinery, and/or process operation in various industries. Data showed that the use of fully immersive VR increased significantly due to the improvements in hardware, display resolution, and affordability. Most of the articles used external assessment to measure the changes in the satisfaction and the declarative knowledge of trainees as these are easier to implement, while some articles started to implement internal assessment that provides an automated assessment capable of measuring complex skills. The results of the study also suggest that it has the potential to improve the training evaluation outcomes compared to traditional training methods. The findings from this study help practitioners and safety managers by providing a training design framework that may be adopted to optimise the condition of a VR-based training.
  • Network analysis of gratitude messages in the learning community

    Masami Yoshida (SpringerOpen, 2022-09-01)
    Abstract In pedagogical practice, gratitude is recognised not as an emotion, but as an approach to learning. This study introduced gratitude messages into the academic online communication of university students and specifically examined the community in which students shared their messages with gratitude. This study examined the tendency of message connections and how gratitude messages prompted replies. To elucidate their connections, exponential random graph models (ERGMs) were used. A post-event questionnaire to evaluate gratitude experiences was also administered. Results revealed that 77.3% of the 172 connected messages from 123 students involved gratitude. When the post-event questionnaire results were examined using an ERGM, the score effects on increasing message connections were found not to be significant. The most prominent indication was a higher level of significant propensities to make mutual connections. The homophily of the message content was found to have a significant propensity to increase connections. The ERGM results and a review of messages revealed that students expressed gratitude for being both benefactors and beneficiaries of gratitude messages, which confirmed their prosocial behaviour.
  • Video-based simulations in teacher education: the role of learner characteristics as capacities for positive learning experiences and high performance

    Michael Nickl; Sina A. Huber; Daniel Sommerhoff; Elias Codreanu; Stefan Ufer; Tina Seidel (SpringerOpen, 2022-09-01)
    Abstract Assessing students on-the-fly is an important but challenging task for teachers. In initial teacher education, a call has been made to better prepare pre-service teachers for this complex task. Advances in technology allow this training to be done through authentic learning environments, such as video-based simulations. To understand the learning process in such simulations, it is necessary to determine how cognitive and motivational learner characteristics influence situative learning experiences, such as the perception of authenticity, cognitive load, and situational motivation, during the simulation and how they affect aspects of performance. In the present study, N = 150 pre-service teachers from German universities voluntarily participated in a validated online video-based simulation targeting on-the-fly student assessments. We identified three profiles of learner characteristics: one with above average knowledge, one with above average motivational-affective traits, and one with below average knowledge and motivational-affective traits. These profiles do not differ in the perception of the authenticity of the simulation. Furthermore, the results indicate that the profiled learners navigate differently through the simulation. The knowledgeable learners tended to outperform learners of the other two profiles by using more learning time for the assessment process, also resulting in higher judgment accuracy. The study highlights how learner characteristics and processes interact, which helps to better understand individual learning processes in simulations. Thus, the findings may be used as a basis for future simulation research with a focus on adaptive and individual support.
  • The influence of digital competences, self-organization, and independent learning abilities on students’ acceptance of digital learning

    Laura Scheel; Gergana Vladova; André Ullrich (SpringerOpen, 2022-08-01)
    Abstract Despite digital learning disrupting traditional learning concepts and activities in higher education, for the successful integration of digital learning, the use and acceptance of the students are essential. This acceptance depends in turn on students’ characteristics and dispositions, among other factors. In our study, we investigated the influence of digital competences, self-organization, and independent learning abilities on students’ acceptance of digital learning and the influence of their acceptance on the resistance to the change from face-to-face to digital learning. To do so, we surveyed 350 students and analyzed the impact of the different dispositions using ordinary least squares regression analysis. We could confirm a significant positive influence of all the tested dispositions on the acceptance of digital learning. With the results, we can contribute to further investigating the underlying factors that can lead to more positive student perceptions of digital learning and build a foundation for future strategies of implementing digital learning into higher education successfully.
  • Assessing digital competence and its relationship with the socioeconomic level of Chilean university students

    Juan Silva-Quiroz; Erla Mariela Morales-Morgado (SpringerOpen, 2022-08-01)
    Abstract Digital competence (DC) is one of the key aspects in citizen development in the digital age. The DC is particularly important in forming university students and future teachers. This article presents the main results of a study to evaluate DC and its relationship with the socioeconomic level of first-year students of pedagogy in three Chilean public universities, located in the north, center, and south of the country. A quantitative research methodology was used, with a sample of 817 students, the data were collected through the DIGCOMP-PED evaluation instrument, which evaluates DC development using the DIGCOMP framework. The results were analyzed at the general and socioeconomic level on the variables of the educational establishment where they attended high school and the territorial area of the university they attended. The main results indicate that the level of DC achievement is intermediate, the areas with the highest levels of achievement were “network security” and “online communication and collaboration.” On the other hand, the lowest levels of achievement were reached in the areas “information and digital literacy,” “digital content creation,” and “problem solving.” The level of DC is higher among students of private establishments and those who attend universities located in the central area.
  • Exploring the relationship between computational thinking and learning satisfaction for non-STEM college students

    Chien Hsiang Liao; Chang-Tang Chiang; I-Chuan Chen; Kevin R. Parker (SpringerOpen, 2022-08-01)
    Abstract While various studies have focused on the significance of computational thinking (CT) for the future career paths of individuals in science, technology, engineering, and mathematics (STEM), few studies have focused on computational thinking for non-STEM college students. This study explores the relationship between computational thinking and learning satisfaction for non-STEM-major college students. A conceptual model is proposed to examine the structural relationships among computational thinking, self-efficacy, self-exploration, enjoyment and learning satisfaction in an AppInventor-based liberal education course. Collecting data from 190 undergraduate students from Taiwan and analyzing the data by using partial least squares (PLS) methods, the research framework confirms the six proposed hypotheses. These results show that both computational thinking and enjoyment play significant roles in both self-exploration and digital self-efficacy. Moreover, digital self-efficacy and self-exploration also have a significant positive influence on learning satisfaction. These findings have implications for influencing the learning outcomes of non-STEM-major college students, computational thinking course instructors, and computational thinking relevant policies.
  • Early-warning prediction of student performance and engagement in open book assessment by reading behavior analysis

    Brendan Flanagan; Rwitajit Majumdar; Hiroaki Ogata (SpringerOpen, 2022-08-01)
    Abstract Digitized learning materials are a core part of modern education, and analysis of the use can offer insight into the learning behavior of high and low performing students. The topic of predicting student characteristics has gained a lot of attention in recent years, with applications ranging from affect to performance and at-risk student prediction. In this paper, we examine students reading behavior using a digital textbook system while taking an open-book test from the perspective of engagement and performance to identify the strategies that are used. We create models to predict the performance and engagement of learners before the start of the assessment and extract reading behavior characteristics employed before and after the start of the assessment in a higher education setting. It was found that strategies, such as: revising and previewing are indicators of how a learner will perform in an open ebook assessment. Low performing students take advantage of the open ebook policy of the assessment and employ a strategy of searching for information during the assessment. Also compared to performance, the prediction of overall engagement has a higher accuracy, and therefore could be more appropriate for identifying intervention candidates as an early-warning intervention system.

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