Online Learning Approach Research Articles

An investigation of online and offline learning models for online Just-in-Time Software Defect Prediction

Just-in-Time Software Defect Prediction (JIT-SDP) operates in an online scenario where additional training data is received over time. Existing online JIT-SDP studies used online Oza ensemble learning methods with Hoeffding Trees as base learners to learn and update JIT-SDP models over time in this scenario. However, it is unknown how these approaches compare against offline learning approaches adapted to operate in online scenarios, and how the use of any other online or offline base learners would affect online JIT-SDP in terms of predictive performance and computational cost. We therefore propose a new approach called Batch Oversampling Rate Boosting (BORB) that is able to use offline base learners in an online JIT-SDP scenario. Based on 10 open source projects, we provide a comprehensive evaluation of BORB with 5 different base learners and the existing online approach Oversampling Rate Boosting with 4 different base learners, both in within-project and cross-project online JIT-SDP scenarios. The results show that offline learning can lead to better predictive performance than the top performing online learning approaches considered in our study, at a higher computational cost. Cross-project data was helpful to improve predictive performance both for offline and online learning, but especially for online learning.

Empowering Communities in Cengkareng through Online Learning: Unveiling the Potential of News Portal Nachannel.my.id

In the contemporary era, news portals have become integral mediums for global information access and dissemination. However, the swift evolution of technology demands a nuanced understanding to effectively harness these platforms. This study employs an online learning approach as part of a community service initiative, targeting the residents of Komplek Kebersihan Street 13th Neighbourhood 10th Hamlet in Cengkareng, West Jakarta. Focused on the news portal Nachannel.my.id, this endeavor not only familiarizes participants with its features but also addresses the imperative of technological literacy in engineering education. By providing insights into news portal functionality and usage, the initiative empowers participants to navigate the digital information landscape proficiently. The study's outcomes underscore the significance of bridging the knowledge gap, as post-service feedback reflects enhanced awareness and appreciation for the advantages conferred by news portals. This research contributes to the field of engineering by emphasizing the pivotal role of technological literacy in engaging with contemporary information platforms.

Digital Learning Response in the Midst of the Covid-19 Pandemic: The Case of Mauritius

This article investigates learners’ perception of their ability to adapt to online learning and the challenges they encountered as well as the opportunities they found in this new e-learning environment, in the midst of the Covid-19 pandemic. A comprehensive survey was undertaken using an online questionnaire circulated among male and female students across various faculties at five universities and business schools in Mauritius. The results indicate that Covid-19 was a major disruption in higher education studies. Despite the online learning approach, students enrolled in STEM degrees were more impacted compared to those in non-STEM degrees. Around one-third of the students asserted that online learning was a major challenge, more so for those in the lowest-income group who lacked the necessary digital tools to adapt to this new learning environment. A non-conducive home environment further had a negative bearing on students’ concentration and ability to cope with their studies. We noted that postgraduate students adapt to digital learning better compared to undergraduates. There is thus a need to rethink, revamp and redesign the education system to be prepared for future shocks and build resilience through the application of best practices in the digitalisation of the education system.

Introversion-extraversion and online course satisfaction

PurposeThe 2020 Coronavirus pandemic forced universities to hastily transition to eLearning on a mass scale, necessitating the identification of populations who are more challenged by the transition. This study aims to identify how students’ level of introversion/extraversion and digital literacy come to play in their satisfaction with the eLearning environment.Design/methodology/approachThe analysis examined 272 Israeli students who moved from a face-to-face learning environment to a Zoom learning environment between March–July 2020, following the outbreak of the pandemic. All the participants completed two rounds of surveys, and 62 of the 272 participants were then interviewed, and their social network was mapped using a sociogram.FindingsFindings indicated that, in accordance with the “poor get richer” hypothesis, introverts expressed more satisfaction from the transition to the video-conferencing Zoom platform than extraverts. In addition, for highly introverted people, high digital literacy was significantly associated with increased course satisfaction, whereas for highly extraverted people, a high number of social ties with peers from the course was associated with course satisfaction.Originality/valueAs expected, the study’s findings shows that there is no “one size fits all” approach for online learning. Learners with different personalities can benefit from learning environments that foster greater satisfaction with the learning experience. Online platforms can, and should, be designed in a way that offers this needed personalization, and this study provides initial principles that can inform such personalization.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2023-0028

Impact of the COVID-19 pandemic on online learning in higher education: a bibliometric analysis

The outbreak of the COVID-19 pandemic significantly disrupted higher education by forcing the transition to online learning, which became a mandatory teaching process during the lockdowns. Although the epidemiological situation has gradually improved since then, online learning is becoming ever more popular as it provides new learning opportunities. Therefore, the paper aims to present recent research trends concerning online learning in higher education during the COVID-19 pandemic by using selected bibliometric approaches. The bibliometric analysis is based on 8,303 documents from the Scopus database published between January 2020 and March 2022, when repeated lockdowns meant most countries were experiencing constant disruptions to the educational process. The results show that the COVID-19 pandemic increased interest in online learning research, notably in English-speaking and Asian countries, with most research being published in open-access scientific journals. Moreover, the topics most frequently discussed in the online learning research during the COVID-19 pandemic were ICT and pedagogy, technology-enhanced education, mental health and well-being, student experience and curriculum and professional development. Finally, the COVID-19 pandemic encouraged explorations of emergency remote learning approaches like e-learning, distance learning and virtual learning, which are intended to limit physical contact between teachers and students, where the specific requirements of a given field of study often guide which online learning approach is the most suitable. The findings add to the existing body of scientific knowledge and support the evidence-based policymaking needed to ensure sustainable higher education in the future.

Comparing in-person, blended and virtual training interventions; a real-world evaluation of HIV capacity building programs in 16 countries in sub-Saharan Africa.

We sought to evaluate the impact of transitioning a multi-country HIV training program from in-person to online by comparing digital training approaches implemented during the pandemic with in-person approaches employed before COVID-19. We evaluated mean changes in pre-and post-course knowledge scores and self-reported confidence scores for learners who participated in (1) in-person workshops (between October 2019 and March 2020), (2) entirely asynchronous, Virtual Workshops [VW] (between May 2021 and January 2022), and (3) a blended Online Course [OC] (between May 2021 and January 2022) across 16 SSA countries. Learning objectives and evaluation tools were the same for all three groups. Across 16 SSA countries, 3023 participants enrolled in the in-person course, 2193 learners participated in the virtual workshop, and 527 in the online course. The proportions of women who participated in the VW and OC were greater than the proportion who participated in the in-person course (60.1% and 63.6%, p<0.001). Nursing and midwives constituted the largest learner group overall (1145 [37.9%] vs. 949 [43.3%] vs. 107 [20.5%]). Across all domains of HIV knowledge and self-perceived confidence, there was a mean increase between pre- and post-course assessments, regardless of how training was delivered. The greatest percent increase in knowledge scores was among those participating in the in-person course compared to VW or OC formats (13.6% increase vs. 6.0% and 7.6%, p<0.001). Gains in self-reported confidence were greater among learners who participated in the in-person course compared to VW or OC formats, regardless of training level (p<0.001) or professional cadre (p<0.001). In this multi-country capacity HIV training program, in-person, online synchronous, and blended synchronous/asynchronous strategies were effective means of training learners from diverse clinical settings. Online learning approaches facilitated participation from more women and more diverse cadres. However, gains in knowledge and clinical confidence were greater among those participating in in-person learning programs.

The Effectiveness of Online Learning in Improving the Electrocardiogram Interpretation Skills of Junior Medical Trainees: A Mixed Methods Observational Study.

Introduction The COVID-19 pandemic hindered medical education and limited access to clinical skills training for trainee medical doctors, including electrocardiogram (ECG) interpretation. These restrictions prompted a shift towards virtual training environments and online learning. In this study, we assessed the impact of the pandemic on trainees' confidence and their perceived difficulty in independently interpreting ECGs. Additionally, we examined the effectiveness of two online learning approaches, namely lectures and case-solving webinars, in improving theirskills. Methods The study was a mixed methods observational study conducted in three phases. In the first phase, a cross-sectional study was conducted to subjectively assess the trainees' confidence levels and the perceived difficulty independently reading ECGs. The second phase involved a cohort study where an online learning module consisting of eight lecture-based sessions was implemented. This module covered all the topics recommended in the foundation doctor training curriculum. The third phase also involved a cohort studywherean online case-based discussion learning module with two problem-solving webinars was introduced. We assessed the outcomes on a 1 to 10 Likert scale for confidence and perceived difficultyin independently reading ECGs. Results Sixty-five trainees participated in the initial cross-sectional study. Among them, 100% of the participants reported substantial difficulty in interpreting ECGs (scoring 6 or more on the Likert scale), and 76.5% of the participants did not feel enough confidence to read ECGs independently (scoring 6 or less). Ten trainees attended the second phase. Online lectures significantly increased the mean confidence score by 1.9 points (t(9) = 2.82, p = 0.02, 95% confidence interval (CI) [0.38-3.42]) and significantly reduced the mean of the perceived difficulty score by 2.7 points (t(9) = 5.71, p < 0.001, 95% CI [1.63-3.77]). Compared to the online lectures, the online problem-solving sessions significantly increased the mean of the composite score of confidence and perceived difficulty in reading ECGs (-0.8 vs. 4 points, 95% CI [1.49, 8.26], p = 0.011). Conclusion The COVID-19 pandemic negatively affected the ECG reading skills of junior medical trainees. However, the online teaching approach effectively improved their confidence and the level of difficulty they experienced in ECG interpretation. Applying online case problem-solving was found to be superior to the lecture-based approach in enhancing these parameters.

Engaging High School Students with Ruangguru: An Innovative Online Learning Approach

This qualitative research investigates the reception of high school students towards the Ruangguru application, a popular online medium of study. The study utilizes in-depth interviews, observations, and literature review to explore students' experiences with the application and its features. Purposive sampling was employed to select informants. The findings reveal that two informants dominate the hegemonic position, indicating their belief in the suitability of the application. Additionally, eight informants highlight the innovative nature of animated delivery in enhancing learning engagement. This research contributes to the understanding of student perspectives on using Ruangguru and has implications for improving online learning experiences globally. Highlights: The study explores the reception of high school students towards the Ruangguru application as an online medium of study, providing insights into its effectiveness and suitability. The research method involves qualitative data collection through in-depth interviews, observations, and literature review, allowing for a comprehensive understanding of students' experiences with the application. The findings reveal the dominant position of two informants who consider the Ruangguru application suitable, while eight informants emphasize the innovative nature of animated content delivery in enhancing student engagement and overcoming fatigue. Keywords: Ruangguru application, high school students, online medium of study, reception, qualitative research

Online (versus face-to-face) augmented reality experience on nursing students’ leg ulcer competency: Two quasi-experimental studies

AimThe aim of this study was two-fold. Firstly, to describe the influence of an augmented reality (AR)-based online educational experience on the academic performance and learning determinants of nursing students related to the study of leg ulcer care. We also set out to compare these online results with those obtained in the same experience developed a year before but in a face-to-face design. BackgroundUndergraduate education in nursing aims to train students towards an acceptable level of competence for professional practice. Sometimes, some essential clinical areas of knowledge are particularly challenging for both students and nurse educators. One of these is the care of chronic wounds, particularly in leg ulcers where nurses play a key role. Currently face-to-face teaching methods are evolving to more active contexts and alternative means such as online learning and AR are becoming widely used, garnering encouraging results. DesignA quasi-experimental design was used. Two experiments were carried out to meet the aims of the study, the first one was a pre-test post-test design in one group and the second one a post-test two experimental groups design. MethodsParticipants were undergraduate nursing students from the School of Nursing of the University of Santiago de Compostela (Spain). The two experimental groups received the same training but in different environments: online during 2019/2020 (n = 111) and onsite during 2018/2019 (n = 72). The instruments selected were a knowledge and skills test and two validated questionnaires. ResultsRegarding academic performance, the study demonstrated significant differences towards learning after the AR-based online experience (Z = −9.074; p ≤ 0.001). The participants also showed good results in relation to the learning determinants studied. Compared with students receiving AR-based face-to-face teaching, the students with the online learning approach scored better in the dimensions Autonomous Learning (U=3104.5; p = 0.020) and 3D Comprehension (U=3167.0; p = 0.035). However, the participants in the onsite experience scored higher in the Attention dimension (U=3163.0; p = 0.035). ConclusionsAR positively influences academic performance and diverse educational variables when this experience is carried out online. Since both the online and face-to-face experience show statistically significant benefits in different dimensions of the learning determinants, the need to improve the AR learning experience through blended-learning environments should be promoted.

When Online Learning Meets ODE: Learning without Forgetting on Variable Feature Space

Machine learning systems that built upon varying feature space are ubiquitous across the world. When the set of practical or virtual features changes, the online learning approach can adjust the learned model accordingly rather than re-training from scratch and has been an attractive area of research. Despite its importance, most studies for algorithms that are capable of handling online features have no ensurance of stationarity point convergence, while the accuracy guaranteed methods are still limited to some simple cases such as L_1 or L_2 norms with square loss. To address this challenging problem, we develop an efficient Dynamic Feature Learning System (DFLS) to perform online learning on the unfixed feature set for more general statistical models and demonstrate how DFLS opens up many new applications. We are the first to achieve accurate &amp; reliable feature-wise online learning for a broad class of models like logistic regression, spline interpolation, group Lasso and Poisson regression. By utilizing DFLS, the updated model is theoretically the same as the model trained from scratch using the entire new feature space. Specifically, we reparameterize the feature-varying procedure and devise the corresponding ordinary differential equation (ODE) system to compute the optimal solutions of the new model status. Simulation studies reveal that the proposed DFLS can substantially ease the computational cost without forgetting.

An Online Learning Approach to Dynamic Pricing and Capacity Sizing in Service Systems

Online Learning in Queueing Systems Most queueing models have no analytic solutions, so previous research often resorts to heavy-traffic analysis for performance analysis and optimization, which requires the system scale (e.g., arrival and service rate) to grow to infinity. In “An Online Learning Approach to Dynamic Pricing and Capacity Sizing in Service Systems,” X. Chen, Y. Liu, and G. Hong develop a new “scale-free” online learning framework designed for optimizing a queueing system, called gradient-based online learning in queue (GOLiQ). GOLiQ prescribes an efficient procedure to obtain improved decisions in successive cycles using newly collected queueing data (e.g., arrival counts, waiting times, and busy times). Besides its robustness in the system scale, GOLiQ is advantageous when focusing on performance optimization in the long run because its data-driven nature enables it to constantly produce improved solutions which will eventually reach optimality. Effectiveness of GOLiQ is substantiated by theoretical regret analysis (with a logarithmic regret bound) and simulation experiments.

An Online Learning Approach to Dynamic Pricing and Capacity Sizing in Service Systems

Online Learning in Queueing Systems Most queueing models have no analytic solutions, so previous research often resorts to heavy-traffic analysis for performance analysis and optimization, which requires the system scale (e.g., arrival and service rate) to grow to infinity. In “An Online Learning Approach to Dynamic Pricing and Capacity Sizing in Service Systems,” X. Chen, Y. Liu, and G. Hong develop a new “scale-free” online learning framework designed for optimizing a queueing system, called gradient-based online learning in queue (GOLiQ). GOLiQ prescribes an efficient procedure to obtain improved decisions in successive cycles using newly collected queueing data (e.g., arrival counts, waiting times, and busy times). Besides its robustness in the system scale, GOLiQ is advantageous when focusing on performance optimization in the long run because its data-driven nature enables it to constantly produce improved solutions which will eventually reach optimality. Effectiveness of GOLiQ is substantiated by theoretical regret analysis (with a logarithmic regret bound) and simulation experiments.

Developing novice analysts’ videofluoroscopic swallowing study skills in speech-language pathology: A randomised control trial comparing blended and online training approaches

Purpose This research investigates the relative effectiveness of independent online and blended learning approaches for novice analysts’ development of videofluoroscopic swallowing study (VFSS) analytical skills. The secondary aims were to explore the impact of training on decision-making and to describe learners’ perspectives of training outcomes. Method Undergraduate speech-language pathology students (n = 74) who had completed the dysphagia academic curriculum in an undergraduate speech-language pathology program were recruited for a randomised control trial. The ability to identify swallowing impairments in adults was compared pre- and post-training across three conditions: independent online (n = 23), peer-supported (n = 23), and expert-facilitated training (n = 28). The training comprised online VFSS training and practice with a commercially available digital video disc (DVD). Result The three training approaches were equal in improving novice analysts’ identification of impairments on VFSS. Participants’ analysis improved pre- to post-training (p = <.001), with no statistical difference amongst training conditions (p = .280). However, the expert facilitation condition resulted in better decision-making skill for novice analysts, as well as higher levels of confidence and greater engagement in the learning. Conclusion Well-designed independent online methods are appropriate to prepare novice analysts for VFSS analytical training. Expert facilitation and peer-supported environments may have benefits for more advanced skill development and engagement, and should be investigated in future studies.

Blockchain-Based Privacy-Aware Contextual Online Learning for Collaborative Edge-Cloud-Enabled Nursing System in Internet of Things

With the rapid growth of Internet of Things (IoT), smart home develops rapidly in these years, which could assist people who need family medical support. It could integrate health care with ambient assisted living (AAL) technologies and provide activities of daily life (ADLs) to the people who need care. This paper proposes a smart home and cross-cloud-and-edge computing based nursing system (NS). In general, a good NS requires low latency, high stability, and the real-time analysis and response, where the conventional centralized cloud computing based approaches cannot meet those requirements very well. To this end, we introduce a novel distributed joint edge-cloud structure to better satisfy these requirements. Moreover, to deal with the security and privacy issues, we introduce the blockchain to verify the identity of data exchanging and differential-privacy (DP) in the NS to protect the healthcare takers’ data privacy. In a word, we propose a privacy-preserving context-aware multi-armed bandit based online learning approach for edge-cloud-enabled NS via blockchain in IoTs. Additionally, our system with a novel top-down expanding tree based structure can support dynamically increasing health care datasets. Extensive experimental and numerical results demonstrate our solution can achieve accurate recommendation results with sublinear regret performance.

A Multisite Randomized Study of an Online Learning Approach to High School Credit Recovery: Effects on Student Experiences and Proximal Outcomes

Online credit recovery will likely expand in the coming years as school districts try to address increased course failure rates brought on by the coronavirus pandemic. Some researchers and policymakers, however, raise concerns over how much students learn in online courses, and there is limited evidence about the effectiveness of online credit recovery. This article presents findings from a multisite randomized study, conducted prior to the pandemic, to expand the field’s understanding of online credit recovery’s effectiveness. Within 24 high schools from a large urban district, the study randomly assigned 1,683 students who failed Algebra 1 or ninth grade English to a summer credit recovery class that either used an online curriculum with in-class teacher support or the school’s business-as-usual teacher-directed class. The results suggest that online credit recovery had relatively insignificant effects on student course experiences and content knowledge, but significantly lower credit recovery rates for English. There was limited heterogeneity in effects across students and schools. Non-response on the study-administered student survey and test limit our confidence in the student experience and content knowledge results, but the findings are robust to different approaches to handling the missing data (multiple imputation or listwise deletion). We discuss how the findings add to the evidence base about online credit recovery and the implications for future research.

Investigation into the transition to online learning due to the COVID-19 pandemic, between new and continuing undergraduate students

Higher Education Institutions (HEIs) were required to rapidly respond to the COVID-19 pandemic, integrating online and blended learning approaches to sustain teaching and learning provision. However, limited evidence exists to understand the student experience and perception of the various methods of online learning, in particular across different levels of study (new and continuing students). Therefore, the aim of this study was to compare the experiences of online learning transition, between new first year undergraduate students and continuing second and third year students, enrolled on various undergraduate sport programmes. A total of 182 students responded to an online survey, which investigated the students’ perceptions of online learning approaches. Participants were split according to level of study; [Level 3 (Foundation Year) and 4 (First Year Undergraduate) combined N = 62, Level 5 (Second Year Undergraduate), N = 51 and Level 6 (Third Year Undergraduate), N = 69]. Key findings highlight that both new and continuing students had an overall negative perception of online learning but did acknowledge that online learning provided a more flexible approach to their overall learning experience compared to face-to-face. Face-to-face teaching was deemed more engaging and sociable, in particular for the practical aspects of the programmes. Overall, there were no significant differences between the different levels of study for any of the questions asked. Although continuing students raised the difficulties of conducting practical sessions online, whereas this was not mentioned by new students. To conclude, this study provides novel insights into the experience of new and continuing students, and we advise that future blended learning strategies should consider the programme as a whole, rather than tailoring pedagogic strategies based on the level of study.

Dynamic Pricing and Placing for Distributed Machine Learning Jobs: An Online Learning Approach

Nowadays distributed machine learning (ML) jobs usually adopt a parameter server (PS) framework to train models over large-scale datasets. Such ML job deploys hundreds of concurrent workers, and model parameter updates are exchanged frequently between workers and PSs. Current practice is that workers and PSs may be placed on different physical servers, bringing uncertainty in jobs’ runtime. Existing cloud pricing policy often charges a fixed price according to the job’s runtime. Although this pricing strategy is simple to implement, such pricing mechanism is not suitable for distributed ML jobs whose runtime is stochastic and can only be estimated according to its placement after job admission. To supplement existing cloud pricing schemes, we design a dynamic pricing and placement algorithm, DPS, for distributed ML jobs. DPS aims to maximize the cloud service provider’s profit, which dynamically calculates unit resource price upon a job’s arrival, and determines job’s placement to minimize its runtime if offered price is accepted to users. Our design exploits the multi-armed bandit (MAB) technique to learn unknown information based on past sales. DPS balances the exploration and exploitation stage, and selects the best price based on the reward which is related to job runtime. Our learning-based algorithm can increase the provider’s profit by 200%, and achieves a sub-linear regret with both the time horizon and the total job number, compared to benchmark pricing schemes. Extensive evaluations using real-world data also validates the efficacy of DPS.

Dynamic Security Assessment of Small-Signal Stability for Power Grids Using Windowed Online Gaussian Process

The online small-signal stability assessment of electrical power grids is typically a challenging problem due to uncertainties and parameter variations of power system dynamics as well as the incurred high computational complexity. This paper proposes a novel theoretical framework for dynamic small-signal stability assessment of power grids by estimating the region of attraction (ROA) for operating states in real time. By analyzing the latest sampling data of power grids in a fixed time window, an up-to-date training set is constructed with the aid of converse Lyapunov function, which enables us to develop an online learning approach based on Gaussian Process (GP) to assess the stability level of power grids. As a result, an iteration algorithm is designed to update the assessment parameters by learning the input-output pairs in the training set. Theoretical analysis is conducted to ensure the existence of converse Lyapunov function for differential-algebraic system that serves to describe power system dynamics, as well as to estimate the region of attraction for operating states with a given confidence level. In particular, a practical method is proposed to leverage time series of phasor measurement unit (PMU) measurements including voltage/current magnitude, phase and frequency (i.e., PMU data) of real power grids for validating the online GP approach. Moreover, validations are taken to substantiate the proposed approach by using PMU data of real smart-grid infrastructure and IEEE test cases. The proposed assessment approach contributes to situational awareness of human operators in the control station, thereby taking proactive remedial actions prior to emergencies. Note to Practitioners—This paper was motivated by the problem of online assessment of power systems security but it also applies to other industrial control systems that have stable state trajectories. Existing approaches to security assessment of power systems generally focus on the adoption of various machine-learning algorithms by treating power grid as a “black box,” which ignores the intrinsic characteristics of power systems and thus restricts the inference performance. This paper proposes a new approach using limited sampling data and system dynamics to construct a domain of stability for power grids, which can reflect the evolution of security zones and provide more accurate predictions. In this work, we mathematically characterize the domain of stability for a practical power grid by analyzing and learning its state trajectories. Then we show how the proposed approach can be efficiently implemented online, which can timely alert human operators to abnormalities. Preliminary validations suggest that this approach is feasible and effective. In future research, we will incorporate it into an energy management system and test it in other industrial processes.

Twenty-First Century Instructional Design: Guiding Vocational Instructors Designing E-Module

Instructional design can be challenging in a vocational education setting, such as in a beauty institution where knowledge has to be balanced with practice, and trained individuals in this area are needed specifically for the new and emerging profession of health and beauty consultant. The e-module was designed using the Dick and Carey model consist of 10 steps. This study uses qualitative approach through semi-structured interviews conducted online with 3 students, 1 instructor and 2 observers from skin analysis class in PIBI Surabaya. The transcribed interview data were analysed using Atlas.tisoftware to produce codes. This study aimed to develop an instructional design for non-formal vocational education as a guide for instructors in creating quality training using an online learning approach. Based on the qualitative results, there are 4 categories that the researcher were asking about, which is media, content, strategy, and student’s response with feasible, practical and effective as the indicators. This study concluded that the instructional design can be used as a reference for instructors in preparing and delivering learning materials where the focus is centred on individual student, with the end goal of achieving competencies (attitude, skill and knowledge/ASK), eight employability skills, five dimensions of competence and twenty-first century skills according to the needs of the industry nowadays.

Reflection on Experiences of First-Year Engineering Students with Blended Flipped Classroom Online Learning during the COVID-19 Pandemic: A Case Study of the Mathematics Course in the Extended Curriculum Program

The shift to remote learning during the COVID-19 pandemic presented a unique challenge for higher education in developing countries such as South Africa, where resources are distributed unevenly. The Department of Chemical Engineering at a South African university of technology used a flipped classroom blended online learning approach in an engineering mathematics course. This study describes the transition of first-year engineering students from face-to-face learning to flipped classroom blended online learning. The Laurillard conversational framework for teaching learning was used to develop the five components of blended learning pedagogy, this allows students to discover, learn, practice, collaborate, and assess (DLPCA). The chosen assessment strategies made use of adaptive learning technology via the WebAssign platform to provide formative and summative assessments as well as timely feedback to each student. The authors examined the remote teaching and learning approach using three indicators: (i) learning—students’ learning experiences; (ii) assessment—students’ academic performance and integrity; and (iii) students’ challenges. The findings had a positive impact on students’ learning and performance in the mathematics course. The limitation to the study was that the data were collected only from one university of technology and were not compared with other universities in the country. The overall findings of this study indicate that students require time to adjust to online pedagogy to ensure a smooth transition.