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The idea to use computers for teaching and learning is over 50 years old. Numerous attempts to use computers for knowledge dissemination under a variety of names have failed in many cases, and have become successful in others. The essence of this paper can be summarized in two sentences. One, in some niches, applications tend to be successful. Second, attempts to fully eliminate humans from the educational process are bound to fail, yet if a large number of aspects is handled well, the role of teachers can indeed be much reduced. The paper aims to discuss these issues.

Report on experimental results.

In some niches, applications of e-Learning technology tend to be successful. However, attempts to fully eliminate humans from the educational process are bound to fail, yet if a large number of aspects is handled well, the role of teachers can indeed be much reduced.

A number of features that seemed essential in earlier e-Learning systems turn out to be superfluous.

New e-Learning systems have to concentrate on quality of content, not complex technology.

E-Learning the right way helps learners, teachers and institutions.

Experiments reported verify or do the opposite of often loosely stated opinions.

The purpose of this systematic review is to highlight some of the business model changes restaurants, bars and beverage producers undertook to modify their operations in order to not only stay in business but also to better serve their employees and communities during the COVID-19 crisis.

An analysis was conducted on 200 industry articles and categorized into three major themes: expansion of take-out/delivery, innovative practices, and community outreach/corporate support, each are further subdivided into additional themes. The systematic review is further supported by personal interviews with industry professionals.

This research finds that there were many different approaches used in adjusting business models in response to the dining restrictions put in place due to COVID-19. From these approaches, themes were developed which resulted in uncovering some suggestions such as developing contingency plans, being flexible and creative, eliminating menu items, investing in a communication platform and getting involved with local government. In addition, some practices operators should be mindful of such as selling gift cards and starting a crowdfund.

This research provides a systematic analysis of business model changes that occurred due to COVID-19 dining restrictions. Researchers can use this information as a guide for further analysis on a specific theme introduced herein.

This research offers several practical implications which will assist the industry should another similar event occur in the future. The systematic analysis describes and documents some suggestions as well as practices to be mindful of in preparing contingency plans for the future.

This research documents an unprecedented time for the hospitality industry by examining how restaurant, bar and beverage producers around the country responded to COVID-19 restrictions. Distilling the multitude of information into succinct themes that highlight the business model changes that occurred will aid future research as well as operators.

The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques.

Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used.

The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way.

Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate.

Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course.

This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.

The objective of the presented pilot study was to test the applicability of a metric to specifically measure performance improvement via a hands-on workshop about collaborative robotics.

Candidates interested in acquiring basic practical skills in working with a collaborative robot completed a distance learning exercise in preparation for a hands-on training workshop. The candidates executed a test before and after the workshop for recording the parameters compiled in the tested performance index (PI).

The results reflected the potential of the tested PI for applications in detecting improvement in practical skill acquisition and revealed potential opportunities for integrating additional performance factors.

The low number of candidates available limited in-depth analyses of the learning outcomes.

The study outcomes provide the basis for follow-up projects with larger cohorts of candidates and control groups in order to expedite the development of technology-assisted performance measurements.

The study contributes to research on performance improvement and prediction of learning outcomes, which is imperative to this emerging field in learning analytics.

The development of the presented PI addresses a scientific gap in learning analytics, i.e. the objective measurement of performance improvement and prediction along skill-intensive training courses. This paper presents an improved version of the PI, which was published at the 12th Conference on Learning Factories, Singapore, April 2022.

The purpose of the study is to analyse municipal solid waste (MSW) disposed of in Jimeta-Yola metropolis for landfill gas (LFG), methane and project viability potential.

The data was collected daily from landfills for four weeks. About 7,329.55 Mg/year of waste was analysed. These waste were separated into bio-degradable components i.e. paper and textile (263.66 Mg), non-food organic (681.45 Mg), wood and straw (189.50 Mg) and food and kitchen waste (1797.20 Mg). Non-degradable components include plastics, polythene bags, metals, sand, stones, cans etc. (4397.73 Mg). The component's characteristics such as a number of samples, weight, volume, landfill age etc. were measured. The waste, methane (CH4) and energy potential were also analysed using LFG energy cost model.

The landfills received 15 Gg/year of MSW and emit 0.31 Gg/year of LFG having CH4 content of 82.95 Mg in 2016. These can produce 33.78 GWh of heat energy equivalent to 10.14 GWh of electricity analytically. Therefore, between 2016 and 2022, about 2.24 Gg CH4 and 5201.32 MWh of electricity were wasted. Henceforth, proper management of these waste substances can produce 186.4 Gg CH4 which will generate 432.52 GWh of electricity. The most economically viable project is an electricity project generating 418 kW/year at a sale price of $1.14/kWh (58.38/kWh) and a payback period of 11 years.

Raw LFG collected can be used in heating brick kilns, boilers, furnaces and greenhouses. When treated, the LFG can produce renewable natural gas (RNG), which is used in energy generation and various domestic, vehicle and industrial applications.

The analytical energy generation can provide gross revenue of ₦19.46bn at an average of ₦192.71million/year. Using Landfill Gas Emissions Model (LandGEM) model, the gross and net revenue will be $0.42m and $0.28m yearly, respectively. The project can provide jobs and economic boost to the immediate community through associated ripple effect.

The research is a pre-feasibility study for LFG to gas or electricity projects in Jimeta-Yola. The study contributed to the body of knowledge as a source of literature for further studies locally and globally.

This study investigated a story-based learning MOOC’s effectiveness in enhancing digital competence.

A quasi-experimental design with 5,501 participants enrolled in a developed MOOC course was assessed through pretests, formative assessments and posttests. K-means clustering, using the Self-Efficacy in Digital Competence Scale (SDCS), was employed to classify experimental and control groups and analyze differences in perceived competence across age groups (10s–60s).

Learners’ digital competence significantly improved (p < 0.001) after the MOOC, demonstrating knowledge and skill gains across various domains. The highest SDCS domain was communication and collaboration, while the lowest was digital content creation. Additionally, the SDCS data showed higher self-efficacy in the 20–40s age group and lower in the 10, 50 and 60s.

The findings suggest a gap in learners’ digital content creation competence. Additional content could be incorporated to bridge this gap. This study supports story-based learning MOOCs for promoting digital competence.

This research contributes to the field by developing and evaluating a MOOC with story-based learning to explore learners’ digital competence and its relationship with age.

This study aimed to measure the effectiveness of online peer-group activism to promote thalassemia prevention among high school students of East Java Indonesia.

Twenty students were recruited as cadres and trained for thalassemia every weekend for four weeks, followed by creating health promotions via online media. The media was further disseminated among the students’ peer groups for a week. The respondent’s knowledge was measured before and after health promotion utilizing an online media mixed-methods approach that combined quantitative data using an online questionnaire and in-depth interviews for qualitative measurement. Descriptive and inferential analyses were performed using Graph Prism 5.00. Interview transcripts were analyzed to elaborate on the respondent’s understanding of thalassemia.

The respondents had good basic knowledge about thalassemia; however, it was not in-line with their understanding, which increased after the online health promotion activity. Therefore, this approach is useful for disseminating health issues during the COVID-19 pandemic and can be implemented for broadening respondents.

This study showed the experience of online peer-group activism for thalassemia prevention in high school students. By empowering the peer group, health promotion is effective in increasing the knowledge and understanding of thalassemia. A similar approach can be proposed for other health issues.