Students’ and Instructor’s Perspective on the use of Blackboard Platform for Delivering an Engineering Course

Uziak, J.; Oladiran, M. Tunde; Lorencowicz, E.; Becker, K.

doi:10.34190/ejel.16.1.2367

Cited by 26 publications

(17 citation statements)

References 31 publications

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“…However, some studies have revealed some issues that may interfere with the ability of students and instructors to collaborate effectively over the Blackboard platform [9,10].…”

Section: A the Use Of Lms In Educationmentioning

confidence: 99%

A Deep Learning Model to Predict Student Learning Outcomes in LMS Using CNN and LSTM

Aljaloud¹,

Uliyan²,

Alkhalil³

et al. 2022

IEEE Access

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Learning Management Systems (LMSs) are increasingly utilized for the administration, tracking, and reporting of educational activities. One such widely used LMS in higher education institutions around the world is Blackboard. This is due to its capabilities of aligning items of learning content, studentstudent and student-teacher interactions, and assessment tasks to specified goals and student learning outcomes. This study aimed to determine how certain Key Performance Indicators (KPIs) based on student interaction with Blackboard helped to forecast the learning outcomes of students. A mixed-methods study design was used which included an analysis of four deep learning models for predicting student performance. Data were collected from reports of seven general preparation courses. They were analyzed using a documentary analysis approach to establish possible predictive KPIs associated with the electronic Blackboard report. Correlational analyses were performed to examine the extent to which these factors are linearly correlated with the performance indicators of students. The results indicated that a predictive model that combined convolutional neural networks and long short-term memory (CNN-LSTM) was the optimal method among the four models tested. The main conclusion drawn from this finding is that the combined CNN-LSTM approach may lead to interventions that optimize and expand the use of the Blackboard LMS in universities.

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“…However, some studies have revealed some issues that may interfere with the ability of students and instructors to collaborate effectively over the Blackboard platform [9,10].…”

Section: A the Use Of Lms In Educationmentioning

confidence: 99%

A Deep Learning Model to Predict Student Learning Outcomes in LMS Using CNN and LSTM

Aljaloud¹,

Uliyan²,

Alkhalil³

et al. 2022

IEEE Access

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“…BB is used worldwide by many colleges and universities with potential benefits (Beatty, 2010). It is considered a flexible and easily accessible educational platform (Uziak et al, 2018;Beatty,2010), and users can access BB at any place via the world wide web (WWW). Among the most distinctive features of BB are efficient functionality, usability, standardization, security, student-teacher interaction, and class management.…”

Section: Blackboard (Bb)mentioning

confidence: 99%

“…Collaboration and communication are regarded as the best LMS features embedded in BB (Bradford et al, 2007;Uziak et al, 2018;Chen, Dobinson & Kent, 2020). Since the teacher-student interaction during the pandemic has been of utmost importance, BB provides access for the teachers to communicate with students by asynchronous and synchronous platforms.…”

Section: Blackboard (Bb)mentioning

confidence: 99%

Descriptive Study of EFL Teachers’ Perception toward E-learning Platforms during the Covid-19 Pandemic

Khalil

Alharbi

2022

EJEL

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The study critically examines the features of three such e-learning platforms: Blackboard, Google Classroom, and Zoom. It provides an in-depth analysis of the digital learning podium's effectiveness in a pandemic like COVID-19. It synthesizes the strengths and limitations of the selected e-learning platforms for English language teaching. The study focuses on authentic e-learning platforms' potentials to encourage EFL teachers and learners to take the challenge of adapting to a transforming world that could engage them in e-learning innovation. An electronic survey of 36 items was conducted to identify EFL teachers' perceptions of these platforms' performance. The survey comprised three questionnaires for each e-learning platform with six parts, namely, accessibility and usability, efficiency and convenience, communication and interaction, teacher's attitude, teacher's satisfaction, and E-learning experience during COVID-19. The elicited data was analysed using Minitab 16 Statistical software. The internal consistency of the survey was measured through the values of Cronbach's Alpha. Other statistical measures such as mean, standard deviation, and agreeable rate (AR) were used to portray the efficacy of each application. The results of the survey indicate that Google Classroom (Ave. AR = 72.92%, Ave. Mean = 3.887) is the most effective e-learning platform, followed by Blackboard (Ave. AR = 68.09%, Ave. Mean = 3.694), and lastly Zoom (Ave. AR = 61.85%, Ave. Mean = 3.544), according to the participants. The research findings should be an initiative for the language learners and help teachers and policymakers design e-learning environments to make the learning process feasible in and out of an international global crisis.

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“…Students emphasise the roles of lecturer interaction and attitude to better deal with their emotions, thereby enhancing the learning process (Hernández-Amorós & Urrea-Solano, 2017). Lecturers have found VLE as an appropriate medium of communication with students that increase their interaction (Uziak et al, 2018). (Hernández-Amorós & Urrea-Solano, 2017) revealed that once lecturers received training on handling students' emotions, students reacted positively to the lecturers' interactions.…”

Section: Lecturer's Interactionmentioning

confidence: 99%

Students's Intention to Use Emotion-Aware Virtual Learning Environment: Does a Lecturer's Interaction Make a Difference?

Nassr

Aldossary

Nasir

2021

Malaysian Journal of Learning and Instruction

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Purpose – This study explored students’ perspective of using emotion-aware Vertual Learning Environment (VLE) in Malaysia’s higher education institutions. The purpose is to investigate the relationships among dimensions of Technology Readiness Index (TRI), attitude, intention to use VLE, and lecturer interaction. The outcomes concerned the emotions involved in the educational process of Malaysia’s higher education institutions. Methodology – Quantitative data were collected via an online survey from 260 students. An empirical analysis was then conducted using structural equation modelling (Smart PLS) in two phases: (1) examining the direct effect of students’ attitude on VLE adoption intention and (2) examining the indirect effect of constructs using lecturer interaction as a mediator. Findings – The findings revealed a significant mediating role of lecturer interaction on the relationship between attitude and intention to use VLE across the student cohort. Inhibitors, such as insecurity and discomfort, were less significant in affecting students’ attitude towards emotion-aware VLE. The results indicate that students are motivated to use VLE when lecturers understand their emotions and react accordingly. Significance – This is one of the studies pertaining to emotions in VLE and lecturer interaction in higher education institutions. The results facilitate an understanding of the pedagogical role of lecturer interaction as a practical learning motivation. It is of particular interest to curriculum and e-learning stakeholders looking to improve students’ interactions with the VLE systems. Apart from extending the current literature, this study has significant practical implications for education management in higher learning institutions. Keywords: Emotion-Aware VLE, Technology Readiness Index (TRI), Attitude, Intention to Use, Lecturer Interaction, online learning, Smart PLS, Higher Education.

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Students’ and Instructor’s Perspective on the use of Blackboard Platform for Delivering an Engineering Course

Cited by 26 publications

References 31 publications

A Deep Learning Model to Predict Student Learning Outcomes in LMS Using CNN and LSTM

A Deep Learning Model to Predict Student Learning Outcomes in LMS Using CNN and LSTM

Descriptive Study of EFL Teachers’ Perception toward E-learning Platforms during the Covid-19 Pandemic

Students's Intention to Use Emotion-Aware Virtual Learning Environment: Does a Lecturer's Interaction Make a Difference?

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