Search results for: challenge based learning

Authors: Ibraheem Alzahrani

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This paper seeks to discover the relationship between both the social constructivist learning theory and the collaborative learning environment. This relationship can be identified through given an example of the learning environment. Due to wiki characteristics, wiki can be used to understand the relationship between constructivist learning theory and collaborative learning environment. However, several evidences will come in this paper to support the idea of why wiki is the suitable method to explore the relationship between social constructivist theory and the collaborative learning and their role in learning. Moreover, learning activities in wiki classroom will be discussed in this paper to find out the result of the learners' interaction in the classroom groups, which will be through two types of communication; synchronous and asynchronous.

Keywords: social constructivist, collaborative, environment, wiki, activities

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Authors: Sakib Hasn, Shahid Anwar

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This study highlights the vital importance of assessing the Simulator Sickness Questionnaire and presence measures as virtual reality (VR) incorporation into language teaching gains popularity. To address user discomfort, which prevents efficient learning in VR environments, the measurement of SSQ becomes crucial. Additionally, evaluating presence metrics is essential to determine the level of engagement and immersion, both crucial for rich language learning experiences. This paper designs a VR-based Chinese language application and proposes a thorough test technique aimed at systematically analyzing SSQ and presence measures. Subjective tests and data analysis were carried out to highlight the significance of addressing user discomfort in VR language education. The results of this study shed light on the difficulties posed by user discomfort in VR language learning and offer insightful advice on how to improve VR language learning applications. Furthermore, the outcome of the research explores ‘VR-based language education,’ ‘inclusive language learning platforms," and "cross-cultural communication,’ highlighting the potential for VR to facilitate language learning across diverse cultural backgrounds. Overall, the analysis results contribute to the enrichment of language learning experiences in the virtual realm and underscore the need for continued exploration and improvement in this field.

Keywords: virtual reality (VR), language education, simulator sickness questionnaire, presence metrics, VR-based Chinese language education

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Authors: Pan Lee Ching, Chua Bee Seok

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The experience of negative work-life interaction often confronted with work related stress includes workload. The appraisal of challenge and hindrance jobs depend on the type of workload to stimulate stress response. Nevertheless, the effects of challenge and hindrance jobs on distress and negative work-life interaction are scarcely explored. Thus, research objective was to examine the relationship among challenge appraisal job (qualitative workload), hindrance appraisal job (quantitative workload), and negative work-life interaction with the mediating role of distress. A survey with random sampling method was performed on current serving public secondary school teachers in Sabah. Collected data showed 447 respondents completed three questionnaires, namely Challenge-hindrance Appraisal Scale, Stress Professional Positive and Negative Questionnaire, and Survey Work-home Interaction-Nijmegan. Partial Least Square-Structural Equation Modeling (PLS-SEM) was used to analyse mediation effect. Results showed distress fully mediates the relationship between challenge appraisal job (qualitative workload) and negative work-life interaction. The indirect effect was significant and negative. While distress partially mediates the relationship between hindrance appraisal job (quantitative workload) and negative work-life interaction. The indirect effect was significant and positive. The study implied that challenge appraisal job could be a positive resource for teacher to facilitate work and life, whereas hindrance appraisal job could disengage the facilitation. Hence, strengthen challenge appraisal job and control hindrance appraisal job could curb distress at work and underpin life interaction among the teachers.

Keywords: challenge-hindrance job, distress, work-life, workload

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Authors: Upasana G. Singh, Meera Gungea

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Learning is no more confined to the traditional classroom, teacher, and student interaction. Many universities offer courses through the Open and Distance Learning (ODL) mode, attracting a diversity of learners in terms of age, gender, and profession to name a few. The ODL mode has surfaced as one of the famous sought-after modes of learning, allowing learners to invest in their educational growth without hampering their personal and professional commitments. This mode of learning, however, requires that those who ultimately choose to adopt it must be prepared to undertake studies through such medium. The purpose of this research is to assess whether students who join universities offering courses through the ODL mode are ready to embark and study within such a framework. This study will be helpful to unveil the challenges students face in such an environment and thus contribute to developing a framework to ease adoption and integration into the ODL environment. Prior to the implementation of e-learning, a readiness assessment is essential for any institution that wants to adopt any form of e-learning. Various e-learning readiness assessment models have been developed over the years. However, this study is based on a conceptual model for e-Learning Readiness Assessment which is a ‘hybrid model’. This hybrid model consists of 4 main parameters: 1) Technological readiness, 2) Culture readiness, 3) Content readiness, and 4) Demographics factors, with 4 sub-areas, namely, technology, innovation, people and self-development. The model also includes the attitudes of users towards the adoption of e-learning as an important aspect of assessing e-learning readiness. For this study, some factors and sub-factors of the hybrid model have been considered and adapted, together with the ‘Attitude’ component. A questionnaire was designed based on the models and students where the target population were students enrolled at the Open University of Mauritius, in undergraduate and postgraduate courses. Preliminary findings indicate that most (68%) learners have an average knowledge about ODL form of learning, despite not many (72%) having previous experience with ODL. Despite learning through ODL 74% of learners preferred hard copy learning material and 48% found difficulty in reading learning material on electronic devices.

Keywords: open learning, distance learning, student readiness, a hybrid model

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Authors: Maslin Masrom, Nik Hasnaa Nik Mahmood, Wan Normeza Wan Zakaria, Azizul Azizan, Norshaliza Kamaruddin

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Traditionally, in science education, the teacher provides facts and the students learn them. It is outmoded for today’s students to equip them with real-life situations, mainly because knowledge and life skills are acquired passively from the instructors. Inquiry-Based Science Education (IBSE) is an approach that allows students to experiment, ask questions, and develop responses based on reasoning. It has provided students and teachers with opportunities to actively engage in collaborative learning via inquiry. This approach inspires the students to become active thinkers, research for solutions, and gain life-long experience and self-confidence. Therefore, the research aims to investigate how the primary-school teacher supports students or pupils through an inquiry-based science education approach for computer science, specifically coding skills. The results are presented and described.

Keywords: inquiry-based science education, student-centered learning, computer science, primary school

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Jennie Long, Marjorie Bock

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Although the professional literature related to Autism Spectrum Disorder (ASD) has focused on successful interventions and strategies, there is a lack of documentation regarding which of these methods and supports are most foundational and essential for classroom use. Specifically, literature does not define the core foundational interventions and strategies that would be elemental for educators to use with students with an ASD diagnosis. From the increase in prevalence of autism spectrum disorders, to the challenge students with ASD pose in classrooms, to the requirement to implement evidence-based practice, rises an enormous challenge in the field of education. Foundational interventions should be in place the first day the student enters the classroom. The nine interventions are foundational in nature and because of the dramatic increase in prevalence there is currently a need for classroom programs to provide the foundation of basic educational skills as well as the specialty skills specific to the area of ASD utilizing the most current research. This article presents nine evidence-based intervention categories for implementation with students on the spectrum.

Keywords: autism spectrum disorder, classroom, evidence-based, foundational

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Authors: Farouk Lawan Gambo

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Data compression shrinks files into fewer bits then their original presentation. It has more advantage on internet because the smaller a file, the faster it can be transferred but learning most of the concepts in data compression are abstract in nature therefore making them difficult to digest by some students (Engineers in particular). To determine the best approach toward learning data compression technique, this paper first study the learning preference of engineering students who tend to have strong active, sensing, visual and sequential learning preferences, the paper also study the advantage that mobility of learning have experienced; Learning at the point of interest, efficiency, connection, and many more. A survey is carried out with some reasonable number of students, through random sampling to see whether considering the learning preference and advantages in mobility of learning will give a promising improvement over the traditional way of learning. Evidence from data analysis using Ms-Excel as a point of concern for error-free findings shows that there is significance different in the students after using learning content provided on smart phone, also the result of the findings presented in, bar charts and pie charts interpret that mobile learning has to be promising feature of learning.

Keywords: data analysis, compression techniques, learning content, traditional learning approach

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Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

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Authors: Roopali Kulkarni, Yuliya Khrypko

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A major problem in educational digital game design is that game developers are often focused on maintaining the fun and playability of an educational game, whereas educators are more concerned with the learning aspect of the game rather than its entertaining characteristics. There is a clear need to understand what key aspects of digital learning games make them an effective learning medium in tertiary education. Through a systematic literature review and content analysis, this paper identifies, evaluates, and summarizes twenty-three key attributes of digital games used in tertiary education and presents a summary digital game-based learning (DGBL) model for designing and evaluating an educational digital game of any genre that promotes effective learning in tertiary education. The proposed solution overcomes limitations of previously designed models for digital game evaluation, such as a small number of game attributes considered or applicability to a specific genre of digital games. The proposed DGBL model can be used to assist game designers and educators with creating effective and engaging educational digital games for the tertiary education curriculum.

Keywords: DGBL model, digital games, educational games, game-based learning, tertiary education

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Authors: Nirit Raichel, Dorit Alt

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Although university teaching is claimed to have a special task to support students in adopting ways of thinking and producing new knowledge anchored in scientific inquiry practices, it is argued that students' habits of learning are still overwhelmingly skewed toward passive acquisition of knowledge from authority sources rather than from collaborative inquiry activities. In order to overcome this critical inadequacy between current educational goals and instructional methods, the LLAF consortium is aimed at developing updated instructional practices that put a premium on adaptability to the emerging requirements of present society. LLAF has created a practical guide for teachers containing updated pedagogical strategies based on the constructivist approach for learning, arranged along Delors’ four theoretical ‘pillars’ of education: Learning to know, learning to do, learning to live together, and learning to be. This presentation will be limited to problem-based learning (PBL), as a strategy introduced in the second pillar. PBL leads not only to the acquisition of technical skills, but also allows the development of skills like problem analysis and solving, critical thinking, cooperation and teamwork, decision- making and self-regulation that can be transferred to other contexts. This educational strategy will be exemplified by a case study conducted in the pre-piloting stage of the project. The case describes a three-fold process implemented in a postgraduate course for in-service teachers, including: (1) learning about PBL (2) implementing PBL in the participants' classes, and (3) qualitatively assessing the contributions of PBL to students' outcomes. An example will be given regarding the ways by which PBL was applied and assessed in civic education for high-school students. Two 9th-grade classes have participated the study; both included several students with learning disability. PBL was applied only in one class whereas traditional instruction was used in the other. Results showed a robust contribution of PBL to students' affective and cognitive outcomes as reflected in their motivation to engage in learning activities, and to further explore the subject. However, students with learning disability were less favorable with this "active" and "annoying" environment. Implications of these findings for the LLAF project will be discussed.

Keywords: problem-based learning, higher education, pedagogical strategies

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Authors: Xun Li, Haojie Wang

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Federated learning is a kind of distributed training and centralized training mode, which is of great value in the protection of user privacy. In order to solve the problem that the model is vulnerable to backdoor attacks in federated learning, a backdoor attack detection method based on a weighted k-medoids algorithm is proposed. First of all, this paper collates the update parameters of the client to construct a vector group, then uses the principal components analysis (PCA) algorithm to extract the corresponding feature information from the vector group, and finally uses the improved k-medoids clustering algorithm to identify the normal and backdoor update parameters. In this paper, the backdoor is implanted in the federation learning model through the model replacement attack method in the simulation experiment, and the update parameters from the attacker are effectively detected and removed by the defense method proposed in this paper.

Keywords: federated learning, backdoor attack, PCA, k-medoids, backdoor defense

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Authors: Anum Ali, Kashaf ad Dooja, Asif Saleem

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The future smart cities trend will be towards Internet of Things (IoT); IoT creates dynamic connections in a ubiquitous manner. Smart cities offer ease and flexibility for daily life matters. By using small devices that are connected to cloud servers based on IoT, network traffic between these devices is growing exponentially, whose security is a concerned issue, since ratio of cyber attack may make the network traffic vulnerable. This paper discusses the latest machine learning approaches in related work further to tackle the increasing rate of cyber attacks, machine learning algorithm is applied to IoT-based network traffic data. The proposed algorithm train itself on data and identify different sections of devices interaction by using supervised learning which is considered as a classifier related to a specific IoT device class. The simulation results clearly identify the attacks and produce fewer false detections.

Keywords: IoT, traffic security, deep learning, classification

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Authors: Jacqueline Żammit

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Online learning has become more popular as a substitute for traditional classroom instruction because of the COVID-19 epidemic. The study aimed to investigate how adult Maltese language learners evaluated the benefits and drawbacks of online instruction. 35 adult participants provided data through semi-structured interviews with open-ended questions. NVivo software was used to analyze the interview data using the thematic analysis method in order to find themes and group the data based on common responses. The advantages of online learning that the participants mentioned included accessing subject content even without live learning sessions, balancing learning with household duties, and lessening vulnerability to problems like fatigue, time-wasting traffic, school preparation, and parking space constraints. Conversely, inadequate Internet access, inadequate IT expertise, a shortage of personal computers, and domestic distractions adversely affected virtual learning. Lack of an Internet connection, IT expertise, a personal computer, or a phone with Internet access caused inequality in access to online learning sessions. Participants thought online learning was a way to resume academic activity, albeit with drawbacks. In order to address the challenges posed by online learning, several solutions are proposed in the research's conclusion.

Keywords: adult learners, online education, e-learning, challenges of online learning, benefits ofonline learning

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Authors: Jie Zhang, Qianyu Guo, Tieyi Zhang, Zhiyong Feng, Xiaohong Li

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With the widespread popularity of mobile devices and the development of artificial intelligence (AI), deep learning (DL) has been extensively applied in Android apps. Compared with traditional Android apps (traditional apps), deep learning based Android apps (DL-based apps) need to use more third-party application programming interfaces (APIs) to complete complex DL inference tasks. However, existing methods (e.g., FlowDroid) for detecting sensitive information leakage in Android apps cannot be directly used to detect DL-based apps as they are difficult to detect third-party APIs. To solve this problem, we design DLtrace; a new static information flow analysis tool that can effectively recognize third-party APIs. With our proposed trace and detection algorithms, DLtrace can also efficiently detect privacy leaks caused by sensitive APIs in DL-based apps. Moreover, using DLtrace, we summarize the non-sequential characteristics of DL inference tasks in DL-based apps and the specific functionalities provided by DL models for such apps. We propose two formal definitions to deal with the common polymorphism and anonymous inner-class problems in the Android static analyzer. We conducted an empirical assessment with DLtrace on 208 popular DL-based apps in the wild and found that 26.0% of the apps suffered from sensitive information leakage. Furthermore, DLtrace has a more robust performance than FlowDroid in detecting and identifying third-party APIs. The experimental results demonstrate that DLtrace expands FlowDroid in understanding DL-based apps and detecting security issues therein.

Keywords: mobile computing, deep learning apps, sensitive information, static analysis

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Authors: Vicario Marina, Bustos Freddy, Olivares Jesús, Gómez Pilar

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This paper presents a didactic model and a tool as educational resources to support the learning of geometry; they focus on topics difficult to understand. The target population is elementary school students. The tool is based on a collaborative educational approach using multi-touch devices. The proposal is based on the challenges found in the instructional design and prototype implementation. Traditionally, elementary students have had many problems assimilating mathematical topics; this new Educatronic prototype facilitates the learning experience using exercises and they were tested with different children demonstrating the benefits of the prototype by improving their mathematical skills.

Keywords: educatronic prototype, geometry, multitouch surface, educational computing, primary school, mathematics, educational informatics

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Authors: Melvin Chan, Chew Lee Teo

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There has been a growing emphasis in elevating the teachers’ proficiency and competencies through continuing professional development (CPD) opportunities. In this era of a Volatile, Uncertain, Complex, Ambiguous (VUCA) world, teachers are expected to be collaborative designers, critical thinkers and creative builders. However, many of the CPD structures are still revolving in the model of transmission, which stands in contradiction to the cultivation of future-ready teachers for the innovative world of emerging technologies. This article puts forward the framing of CPD through a Principle-Based, Technological-Driven Knowledge Building Approach grounded in the essence of andragogy and progressive learning theories where growth is best exemplified through an authentic immersion in a social/community experience-based setting. Putting this Knowledge Building Professional Development Model (KBPDM) in operation via a Professional Learning Team (PLT) situated in a Secondary School in Singapore, research findings reveal that the intervention has led to a fundamental change in the learning paradigm of the teachers, henceforth equipping and empowering them successfully in their pedagogical design and practices for a 21^st century classroom experience. This article concludes with the possibility in leveraging the Learning Analytics to deepen the CPD experiences for educators.

Keywords: continual professional development, knowledge building, learning paradigm, principle-based

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Authors: Jihen Bennaceur, Wissem Zouaghi, Ali Mabrouk

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The major aim of cyber threat intelligence (CTI) is to provide sophisticated knowledge about cybersecurity threats to ensure internal and external safeguards against modern cyberattacks. Inaccurate, incomplete, outdated, and invaluable threat intelligence is the main problem. Therefore, data analysis based on AI algorithms is one of the emergent solutions to overcome the threat of information-sharing issues. In this paper, we propose a supervised machine learning-based algorithm to improve threat information sharing by providing a sophisticated classification of cyber threats and data. Extensive simulations investigate the accuracy, precision, recall, f1-score, and support overall to validate the designed algorithm and to compare it with several supervised machine learning algorithms.

Keywords: threat information sharing, supervised learning, data classification, performance evaluation

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Authors: Vani Singhal, Jitendra Parmar, Satyendra Singh Chouhan

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Unseen class discovery has now become an important part of a machine-learning algorithm to judge new classes. Unseen classes are the classes on which the machine learning model is not trained on. With the advancement in technology and AI replacing humans, the amount of data has increased to the next level. So while implementing a model on real-world examples, we come across unseen new classes. Our aim is to find the number of unseen classes by using a hierarchical-based active learning algorithm. The algorithm is based on hierarchical clustering as well as active sampling. The number of clusters that we will get in the end will give the number of unseen classes. The total clusters will also contain some clusters that have unseen classes. Instead of first discovering unseen classes and then finding their number, we directly calculated the number by applying the algorithm. The dataset used is for intent classification. The target data is the intent of the corresponding query. We conclude that when the machine learning model will encounter real-world data, it will automatically find the number of unseen classes. In the future, our next work would be to label these unseen classes correctly.

Keywords: active sampling, hierarchical clustering, open world learning, unseen class discovery

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Authors: N. Bolong, J. Makinda, I. Saad

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Laboratory activities have produced benefits in student learning. With current drives of new technology resources and evolving era of education methods, renewal status of learning and teaching in laboratory methods are in progress, for both learners and the educators. To enhance learning outcomes in laboratory works particularly in engineering practices and testing, learning via hands-on by instruction may not sufficient. This paper describes and compares techniques and implementation of traditional (expository) with open-ended laboratory (problem-based) for two consecutive cohorts studying environmental laboratory course in civil engineering program. The transition of traditional to problem-based findings and effect were investigated in terms of course assessment student feedback survey, course outcome learning measurement and student performance grades. It was proved that students have demonstrated better performance in their grades and 12% increase in the course outcome (CO) in problem-based open-ended laboratory style than traditional method; although in perception, students has responded less favorable in their feedback.

Keywords: engineering education, open-ended laboratory, environmental engineering lab

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Authors: Caridad Maylin-Aguilar, Beatriz Duarte-Monedero

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Problem-based learning (PBL) is a useful tool for adult and practice oriented audiences, as University students. As a consequence of the huge disruption caused by the COVID pandemic in the hospitality industry, hotels of all categories closed down in Spain from March 2020. Since that moment, the luxury segment was blooming with optimistic prospects for new openings. Hence, Hospitality students were expecting a positive situation in terms of employment and career development. By the beginning of the 2020-21 academic year, these expectations were seriously harmed. By October 2020, only 9 of the 32 hotels in the luxury segment were opened with an occupation rate of 9%. Shortly after, the evidence of a second wave affecting especially Spain and the homelands of incoming visitors bitterly smashed all forecasts. In accordance with the situation, a team of four professors and practitioners, from four different subject areas, developed a real case, inspired in one of these hotels, the 5-stars Emperatriz by Barceló. Students in their 2nd course were provided with real information as marketing plans, profit and losses and operational accounts, employees profiles and employment costs. The challenge for them was to act as consultants, identifying potential courses of action, related to best, base and worst case. In order to do that, they were organized in teams and supported by 4th course students. Each professor deployed the problem in their subject; thus, research on the customers behavior and feelings were necessary to review, as part of the marketing plan, if the current offering of the hotel was clear enough to guarantee and to communicate a safe environment, as well as the ranking of other basic, supporting and facilitating services. Also, continuous monitoring of competitors’ activity was necessary to understand what was the behavior of the open outlets. The actions designed after the diagnose were ranked in accordance with their impact and feasibility in terms of time and resources. Also they must be actionable by the current staff of the hotel and their managers and a vision of internal marketing was appreciated. After a process of refinement, seven teams presented their conclusions to Emperatriz general manager and the rest of professors. Four main ideas were chosen, and all the teams, irrespectively of authorship, were asked to develop them to the state of a minimum viable product, with estimations of impacts and costs. As the process continues, students are nowadays accompanying the hotel and their staff in the prudent reopening of facilities, almost one year after the closure. From a professor’s point of view, key learnings were 1.- When facing a real problem, a holistic view is needed. Therefore, the vision of subjects as silos collapses, 2- When educating new professionals, providing them with the resilience and resistance necessaries to deal with a problem is always mandatory, but now seems more relevant and 3.- collaborative work and contact with real practitioners in such an uncertain and changing environment is a challenge, but it is worth when considering the learning result and its potential.

Keywords: problem-based learning, hospitality recovery, collaborative learning, resilience

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Authors: Piyanut Suchit

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The purposes of this study was to investigate students’ satisfaction learning with the web based instruction on the puppet show. The population of this study includes 53 students in the Program of Library and Information Sciences who registered in the subject of Puppet for Assisting Learning Development in semester 2/2011, Suansunandha Rajabhat University, Bangkok, Thailand. The research instruments consist of web based instruction on the puppet show, and questionnaires for students’ satisfaction. The research statistics includes arithmetic mean, and standard deviation. The results revealed that the students reported very high satisfaction with mean = 4.63, SD = 0.52, on the web based instruction.

Keywords: puppet show, web based instruction, satisfaction, Suansunandha Rajabhat University

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Authors: Muhamamd Shahid Farooq, Maazan Zafar, Rizawana Akhtar

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E-learning technologies are the blessings of advancements in science and technology. These facilitate the learners to get information at any place and any time by improving their self-confidence, self-efficacy and effectiveness in teaching learning process. E-learning provides an individualized learning experience for learners and remove barriers faced by students during new and creative ways of gaining information. It provides a wide range of facilities to enable the teachers and students for effective and purposeful learning. This study was conducted to explore the attitudes of university students and teachers towards e-learning working in a metropolitan university of Pakistan. The personal, institutional and technological characteristics of the teachers and students of higher education institution effect the adoption of e-learning. For this descriptive study 449 students and 35 university teachers were surveyed by using a Likert scale type questionnaire consisting of 52 statements relating to six factors "perceived usefulness, intention to adopt e-learning, ease of e-learning use, availability resources, e-learning stressors, and pressure to use e-learning". Data were analyzed by making comparisons on the basis of different demographic factors. The findings of the study show that both type of respondents have positive attitude towards e-learning. However, the male and female respondents differ in their opinion for e-learning implementation.

Keywords: e-learning, ICT, e-sources of learning, questionnaire

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Authors: Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Shih-Ye Wang, Kuo-Chung Chu, Chien-Yeh Hsu

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Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predicting and preventing complications of chronic kidney disease (CKD). This study aimed to develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and effective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportional hazard regression analyses were performed to determine the variables with high prognostic values for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laboratory, laboratory, and metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, body mass index (BMI), and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have demonstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The machine learning models are simple to use and flexible because they work even with incomplete data and can be applied in any clinical setting, including settings where laboratory data is difficult to obtain.

Keywords: chronic kidney disease, glomerular filtration rate, creatinine, novel metabolic indices, machine learning, risk prediction

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Authors: Salina Budin, Shaira Ismail

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Students learn effectively in a learning environment with a suitable teaching approach that matches their learning preferences. The main objective of the study is to examine the learning preferences amongst the students in the Science and Technology (S&T), and Social Science (SS) fields of study at the Universiti Teknologi Mara (UiTM), Pulau Pinang. The measurement instrument is based on the Dunn and Dunn Learning Styles which measure five elements of learning styles; environmental, sociological, emotional, physiological and psychological. Questionnaires are distributed amongst undergraduates in the Faculty of Mechanical Engineering and Faculty of Business Management. The respondents comprise of 131 diploma students of the Faculty of Mechanical Engineering and 111 degree students of the Faculty of Business Management. The results indicate that, both S&T and SS students share a similar learning preferences on the environmental aspect, emotional preferences, motivational level, learning responsibility, persistent level in learning and learning structure. Most of the S&T students are concluded as analytical learners and the majority of SS students are global learners. Both S&T and SS students are concluded as visual learners, preferred to be in an active mobility in a relaxing and enjoying mode with some light of refreshments during the learning process and exhibited reflective characteristics in learning. Obviously, the S&T students are considered as left brain dominant, whereas the SS students are right brain dominant. The findings highlighted that both categories of students exhibited similar learning preferences except on psychological preferences.

Keywords: learning preferences, Dunn and Dunn learning style, teaching approach, science and technology, social science

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Authors: Wasila Lawan Gadanya

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This paper examines the problems of learning English vowel pronunciation. The objective is to identify some of the factors that affect the learning of English vowel sounds and their proper realization in words. The theoretical framework adopted is based on both error analysis and contrastive analysis. The data collection instruments used in the study are questionnaire and word list for the respondents (students) and observation of some of their lecturers. All the data collected were analyzed using simple percentage. The findings show that it is not a single factor that affects the learning of English vowel pronunciation rather many factors concurrently do so. Among the factors examined, it has been found that lack of correlation between English orthography and its pronunciation, not mother-tongue (which most people consider as a factor affecting learning of the pronunciation of a second language), has the greatest influence on students’ learning and realization of English vowel sounds since the respondents in this study are from different ethnic groups of Nigeria and thus speak different languages but having the same or almost the same problem when pronouncing the English vowel sounds.

Keywords: English vowels, learning, Nigeria, pronunciation

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Authors: Abdool Qaiyum Mohabuth, Syed Munir Ahmad

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Assessment enables students to focus on their learning, assessment. It engages them to work hard and motivates them in devoting time to their studies. Student learning is directly influenced by the type of assessment involved in the programme. Summative Assessment aims at providing measurement of student understanding. In fact, it is argued that summative assessment is used for reporting and reviewing, besides providing an overall judgement of achievement. While summative assessment is a well defined process for learning that takes place in the classroom environment, its application within the practice environment is still being researched. This paper discusses findings from a mixed-method study for exploring the effectiveness of summative assessment in practice learning. A survey questionnaire was designed for exploring the perceptions of mentors and students about summative assessment in practice learning. The questionnaire was administered to the University of Mauritius students and mentors who supervised students for their Work-Based Learning (WBL) practice at the respective placement settings. Some students, having undertaken their WBL practice, were interviewed, for capturing their views and experiences about the application of summative assessment in practice learning. Semi-structured interviews were also conducted with three experienced mentors who have assessed students on practice learning. The findings reveal that though learning in the workplace is entirely different from learning at the University, most students had positive experiences about their summative assessments in practice learning. They felt comfortable and confident to be assessed by their mentors in their placement settings and wished that the effort and time that they devoted to their learning be recognised and valued. Mentors on their side confirmed that the summative assessment is valid and reliable, enabling them to better monitor and coach students to achieve the expected learning outcomes.

Keywords: practice learning, judgement, summative assessment, knowledge, skills, workplace

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Authors: Liu Yi Hui

Abstract:

The COVID-19 pandemic in 2020 has globally affected all aspects of life, with social distancing and quarantine orders causing turmoil and learning in community colleges being temporarily paused. In fact, this is the first time that adult education has faced such a severe challenge. It forces researchers to reflect on the impact of pandemics on adult education and ways to respond. Distance learning appears to be one of the pedagogical tools capable of dealing with interpersonal isolation and social distancing caused by the pandemic. This research aims to examine whether the impact of social distancing during COVID-19 will lead to increased acceptance of technology and, subsequently, an increase in adults ’ willingness to participate in distance learning. The hypothesis that social distancing and the desire to participate in distance learning affects learners’ tendency to accept technology is investigated. Teachers ’ participation in distance education and acceptance of technology are used as adjustment variables with the relationship to “social distancing,” “participation in distance learning,” and “acceptance of technology” of learners. A questionnaire survey was conducted over a period of twelve months for teachers and learners at all community colleges in Taiwan who enrolled in a basic unit course. Community colleges were separated using multi-stage cluster sampling, with their locations being metropolitan, non-urban, south, and east as criteria. Using the G*power software, 660 samples were selected and analyzed. The results show that through appropriate pedagogical strategies or teachers ’ own acceptance of technology, adult learners’ willingness to participate in distance learning could be influenced. A diverse model of participation can be developed, improving adult education institutions’ ability to plan curricula to be flexible to avoid the risk associated with epidemic diseases.

Keywords: social distancing, adult learning, community colleges, technology acceptance model

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Authors: Patrick Mapulanga, Chisomo Petros Ganya

Abstract:

Purpose: This study aimed to investigate the integration of Simulation-Based Education (SBE) into nursing and midwifery curricula in resource-constrained countries using Malawi as a case study. The purpose of this study is to assess the extent to which SBE is mentioned in curricula and explore the associated content, assessment criteria, and guidelines. Methodology: The research methodology involved a desk study of nursing and midwifery curricula in Malawi. A comprehensive review was conducted to identify references to SBE by examining documents such as official curriculum guides, syllabi, and educational policies. The focus is on understanding the prevalence of SBE without delving into the specific content or assessment details. Findings: The findings revealed that SBE is indeed mentioned in the nursing and midwifery curricula in Malawi; however, there is a notable absence of detailed content and assessment criteria. While acknowledgement of SBE is a positive step, the lack of specific guidelines poses a challenge to its effective implementation and assessment within the educational framework. Conclusion: The study concludes that although the recognition of SBE in Malawian nursing and midwifery curricula signifies a potential openness to innovative learning strategies, the absence of detailed content and assessment criteria raises concerns about the practical application of SBE. Addressing this gap is crucial for harnessing the full transformative potential of SBE in resource-constrained environments. Areas for Further Research: Future research endeavours should focus on a more in-depth exploration of the content and assessment criteria related to SBE in nursing and midwifery curricula. Investigating faculty perspectives and students’ experiences with SBE could provide valuable insights into the challenges and opportunities associated with its implementation. Study Limitations and Implications: The study's limitations include reliance on desk-based analysis, which limits the depth of understanding regarding SBE implementation. Despite this constraint, the implications of the findings underscore the need for curriculum developers, educators, and policymakers to collaboratively address the gaps in SBE integration and ensure a comprehensive and effective learning experience for nursing and midwifery students in resource-constrained countries.

Keywords: simulation based education, transformative learning, nursing and midwifery, curricula, Malawi

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Authors: Gokhan Egilmez, Erika Hatfield, Julie Turner

Abstract:

In this study, we investigated the impacts of service-learning integration on an undergraduate level business analytics course from multiple perspectives, including academic proficiency, community awareness, engagement, social responsibility, and reflection. We assessed the impact of the service-learning experience by using a survey developed primarily based on the literature review and secondarily on an ad hoc group of researchers. Then, we implemented the survey in two sections, where one of the sections was a control group. We compared the results of the empirical survey visually and statistically.

Keywords: business analytics, service learning, experiential education, statistical analysis, survey research

Procedia PDF Downloads 84