Search results for: learning methods

Authors: Stanle Madonsela

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For learning to take place effectively, learners have to use language. Language becomes a critical tool by which to communicate, to express feelings, desires and thoughts, and most of all to learn. However, each individual’s capacity to use language is unique. In multilingual countries, classrooms usually comprise learners from different language backgrounds, and therefore the language used for teaching and learning requires rethinking. Interaction in the classroom, if done in a language that is understood by the learners, could maximise the outcomes of learning. This paper explores the extent to which the use of a single code becomes a source of anxiety to learners in multilingual classrooms in South African schools. It contends that a multilingual approach in the learning process should be explored in order to promote learner autonomy in the learning process.

Keywords: anxiety, classroom, foreign language teaching, multilingual

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Authors: Ashutosh Mishra, Nikhil Goyal

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A 3D facial emotion recognition model based on deep learning is proposed in this paper. Two convolution layers and a pooling layer are employed in the deep learning architecture. After the convolution process, the pooling is finished. The probabilities for various classes of human faces are calculated using the sigmoid activation function. To verify the efficiency of deep learning-based systems, a set of faces. The Kaggle dataset is used to verify the accuracy of a deep learning-based face recognition model. The model's accuracy is about 65 percent, which is lower than that of other facial expression recognition techniques. Despite significant gains in representation precision due to the nonlinearity of profound image representations.

Keywords: facial recognition, computational intelligence, convolutional neural network, depth map

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Authors: May George

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Inference plays an important role in the learning process and it can lead to a rapid acquisition of a second language. When learning a non-native language, i.e., a critical language like Arabic, the students depend on the teacher’s support most of the time to learn new concepts. The students focus on memorizing the new vocabulary and stress on learning all the grammatical rules. Hence, the students became mechanical and cannot produce the language easily. As a result, they are unable to predict the meaning of words in the context by relying heavily on the teacher, in that they cannot link their prior knowledge or even identify the meaning of the words without the support of the teacher. This study explores how the teacher guides students learning during the inference process and what are the processes of learning that can direct student’s inference.

Keywords: inference, reading, Arabic, language acquisition

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Authors: K. M. Wong

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Engineering education is crucial to technological innovation and advancement worldwide by generating young talents who are able to integrate scientific principles and design practical solutions for real-world problems. Graduates of engineering curriculums are expected to demonstrate an extensive set of learning outcomes as required in international accreditation agreements for engineering academic qualifications, such as the Washington Accord and the Sydney Accord. On the other hand, students have different learning preferences of receiving, processing and internalizing knowledge and skills. If the learning environment is advantageous to the learning styles of the students, there is a higher chance that the students can achieve the intended learning outcomes. With proper identification of the learning styles of the students, corresponding teaching strategies can then be developed for more effective learning. This research was an investigation of learning styles of tertiary students studying higher diploma programmes in Hong Kong. Data from over 200 students in engineering programmes were collected and analysed to identify the learning characteristics of students. A small-scale longitudinal study was then started to gather academic results of the students throughout their two-year engineering studies. Preliminary results suggested that the sample students were reflective, sensing, visual, and sequential learners. Observations from the analysed data not only provided valuable information for teachers to design more effective teaching strategies, but also provided data for further analysis with the students’ academic results. The results generated from the longitudinal study shed light on areas of improvement for more effective engineering curriculum design for better teaching and learning.

Keywords: learning styles, learning characteristics, engineering education, vocational education, Hong Kong

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Authors: Sai Maniveer Adapa, Sai Guptha Perla, Adithya Reddy P.

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Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords: thyroid tumor diagnosis, ultrasound images, deep learning, machine learning, convolutional auto-encoder, support vector machine

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Authors: Saurabh Kumar

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In the fiercely competitive landscape of e-commerce, understanding and mitigating customer churn has become paramount for sustainable business growth. This paper presents a thorough investigation into the application of machine learning techniques for churn prediction in e-commerce, aiming to provide actionable insights for businesses seeking to enhance customer retention strategies. We conduct a comparative study of various machine learning algorithms, including traditional statistical methods and ensemble techniques, leveraging a rich dataset sourced from Kaggle. Through rigorous evaluation, we assess the predictive performance, interpretability, and scalability of each method, elucidating their respective strengths and limitations in capturing the intricate dynamics of customer churn. We identified the XGBoost classifier to be the best performing. Our findings not only offer practical guidelines for selecting suitable modeling approaches but also contribute to the broader understanding of customer behavior in the e-commerce domain. Ultimately, this research equips businesses with the knowledge and tools necessary to proactively identify and address churn, thereby fostering long-term customer relationships and sustaining competitive advantage.

Keywords: customer churn, e-commerce, machine learning techniques, predictive performance, sustainable business growth

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Authors: S. Probine

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The visual arts have been proven to be a central means through which young children can communicate their ideas, reflect on experience, and construct new knowledge. Despite this, perceptions of, and the degree to which the visual arts are valued within education, vary widely within political, educational, community and family contexts. These differing perceptions informed my doctoral research project, which explored the contextual factors that affect how young children come to value and use the visual arts in their lives and learning. The qualitative methodology of narrative inquiry with inclusion of arts-based methods was most appropriate for this inquiry. Using a sociocultural framework, the stories collected were analysed through the sociocultural theories of Lev Vygotsky as well as the work of Urie Bronfenbrenner, together with postmodern theories about identity formation. The use of arts-based methods such as teacher’s reflective art journals and the collection of images by child participants and their parent/caregivers allowed the research participants to have a significant role in the research. Three early childhood settings at which the visual arts were deeply valued as a meaning-making device in children’s learning, were purposively selected to be involved in the research. At each setting, the study found a unique and complex web of influences and interconnections, which shaped how children utilised the visual arts to mediate their thinking. Although the teachers' practices at all three centres were influenced by sociocultural theories, each settings' interpretations of these theories were unique and resulted in innovative interpretations of the role of the teacher in supporting visual arts learning. These practices had a significant impact on children’s experiences of the visual arts. For many of the children involved in this study, visual art was the primary means through which they learned. The children in this study used visual art to represent their experiences, relationships, to explore working theories, their interests (including those related to popular culture), to make sense of their own and other cultures, and to enrich their imaginative play. This research demonstrates that teachers have fundamental roles in fostering and disseminating the importance of the visual arts within their educational communities.

Keywords: arts-based methods, early childhood education, teacher's visual arts pedagogies, visual arts

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Authors: Jiaqi Huang, Yuheng Wang

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Premises selection, the task of selecting a set of axioms for proving a given conjecture, is a major bottleneck in automated theorem proving. An array of deep-learning-based methods has been established for premises selection, but a perfect performance remains challenging. Our study examines the inaccuracy of deep neural networks in premises selection. Through training network models using encoded conjecture and axiom pairs from the Mizar Mathematical Library, two potential biases are found: the network models classify more premises as necessary than unnecessary, referred to as the ‘positive bias’, and the network models perform better in proving conjectures that paired with more axioms, referred to as ‘length bias’. The ‘positive bias’ and ‘length bias’ discovered could inform the limitation of existing deep neural networks.

Keywords: automated theorem proving, premises selection, deep learning, interpreting deep learning

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Authors: Carol C. Opara, Olukemi E. Adetuyi-Olu-Francis

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The need for ICT in Teacher Education due to the nature of 21st-century learners who are computer citizens is essential. The recent increase in the use of Mobile phones has equally revealed the importance of Mobile Applications for learning purposes. However, teacher-trainees and the trainers need to be well-grounded in basic ICT skills for an appropriate outcome. This study seeks to assess the use of Mobile Applications for language learning in Teacher Education teaching-learning process. A 22-item e-questionnaire was used to elicit information from teacher-trainers and teachers-trainees from Faculties of Education in Nigerian Universities. Major findings of this study include: That teacher-education sector is not adequately prepared for manipulative use of ICT and Mobile Applications for teaching and learning process; etc. It was recommended among others that, teacher-trainers should be trained and re-trained on the manipulative use of Mobile devices and the several applications for teaching-learning purpose, especially language education.

Keywords: information and communications technology, ICT, language learning, mobile application, sustainable development, teacher education

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Authors: Gilbert Makanda, Roelf Sypkens

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A mathematical model for knowledge acquisition in teaching and learning is proposed. In this study we adopt the mathematical model that is normally used for disease modelling into teaching and learning. We derive mathematical conditions which facilitate knowledge acquisition. This study compares the effects of dropping out of the course at early stages with later stages of learning. The study also investigates effect of individual interaction and learning from other sources to facilitate learning. The study fits actual data to a general mathematical model using Matlab ODE45 and lsqnonlin to obtain a unique mathematical model that can be used to predict knowledge acquisition. The data used in this study was obtained from the tutorial test results for mathematics 2 students from the Central University of Technology, Free State, South Africa in the department of Mathematical and Physical Sciences. The study confirms already known results that increasing dropout rates and forgetting taught concepts reduce the population of knowledgeable students. Increasing teaching contacts and access to other learning materials facilitate knowledge acquisition. The effect of increasing dropout rates is more enhanced in the later stages of learning than earlier stages. The study opens up a new direction in further investigations in teaching and learning using differential equations.

Keywords: differential equations, knowledge acquisition, least squares nonlinear, dynamical systems

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Authors: Gorkem Algan, Ilkay Ulusoy, Saban Gonul, Banu Turgut, Berker Bakbak

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Computer vision systems recently made a big leap thanks to deep neural networks. However, these systems require correctly labeled large datasets in order to be trained properly, which is very difficult to obtain for medical applications. Two main reasons for label noise in medical applications are the high complexity of the data and conflicting opinions of experts. Moreover, medical imaging datasets are commonly tiny, which makes each data very important in learning. As a result, if not handled properly, label noise significantly degrades the performance. Therefore, a label-noise-robust learning algorithm that makes use of the meta-learning paradigm is proposed in this article. The proposed solution is tested on retinopathy of prematurity (ROP) dataset with a very high label noise of 68%. Results show that the proposed algorithm significantly improves the classification algorithm's performance in the presence of noisy labels.

Keywords: deep learning, label noise, robust learning, meta-learning, retinopathy of prematurity

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Chi H. Lin, Chi C. Lin, Chih L. Hsieh

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Motor Imagery (MI) alone or combined with action observation (AO) has been shown to enhance motor performance and skill learning. The most effective way to combine these techniques has received limited scientific scrutiny. In the present study, we examined the effects of simultaneous (i.e., observing an action whilst imagining carrying out the action concurrently), alternate (i.e., observing an action and then doing imagery related to that action consecutively) and synthesis (alternately perform action observation and imagery action and then perform observation and imagery action simultaneously) AOMI combinations on improving golf putting performance and learning. Participants, 45 university students who had no formal experience of using imagery for the study, were randomly allocated to one of four training groups: simultaneous action observation and motor imagery (S-AOMI), alternate action observation and motor imagery (A-AOMI), synthesis action observation and motor imagery (A-S-AOMI), and a control group. And it was applied 'Different Experimental Groups with Pre and Post Measured' designs. Participants underwent eighteen times of different interventions, which were happened three times a week and lasting for six weeks. We analyzed the information we received based on two-factor (group × times) mixed between and within analysis of variance to discuss the real effects on participants' golf putting performance and learning about different intervention methods of different types of combined action observation and motor imagery. After the intervention, we then used imagery questionnaire and journey to understand the condition and suggestion about different motor imagery and action observation intervention from the participants. The results revealed that the three experimental groups both are effective in putting performance and learning but not for the control group, and the A-S-AOMI group is significantly better effect than S-AOMI group on golf putting performance and learning. The results confirmed the effect of motor imagery combined with action observation on the performance and learning of golf putting. In particular, in the groups of synthesis, motor imagery, or action observation were alternately performed first and then performed motor imagery, and action observation simultaneously would have the best effectiveness.

Keywords: motor skill learning, motor imagery, action observation, simulation

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Authors: Omid Noroozi

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Digital dialogue games have opened up opportunities for learning skills by engaging students in complex problem solving that mimic real world situations, without importing unwanted constraints and risks of the real world. Digital dialogue games can be motivating and engaging to students for fun, creative thinking, and learning. This study explored how undergraduate students engage with argumentative discourse activities which have been designed to intensify debate. A pre-test, post-test design was used with students who were assigned to groups of four and asked to debate a controversial topic with the aim of exploring various 'pros and cons' on the 'Genetically Modified Organisms (GMOs)'. Findings reveal that the Digital dialogue game can facilitate argumentation-based learning. The digital Dialogue game was also evaluated positively in terms of students’ satisfaction and learning experiences.

Keywords: argumentation, dialogue, digital game, learning, motivation

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Authors: Dohun Kim

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Technological innovations have stimulated flipped learning in many disciplines, including language teaching. It is a specific type of blended learning, which combines onsite (i.e. face-to-face) with online experiences to produce effective, efficient and flexible learning. Flipped learning literally ‘flips’ conventional teaching and learning activities upside down: it leverages technologies to deliver a lecture and direct instruction—other asynchronous activities as well—outside the classroom to reserve onsite time for interaction and activities in the upper cognitive realms: applying, analysing, evaluating and creating. Unlike the conventional flipped approaches, which focused on video lecture, followed by face-to-face or on-site session, new innovative methods incorporate various means and structures to serve the needs of different academic disciplines and classrooms. In the light of such innovations, this study adopted ‘student-engaged’ approaches to interpreter training and contrasts them with traditional classrooms. To this end, students were also encouraged to engage in asynchronous activities online, and innovative technologies, such as Telepresence, were employed. Based on the class implementation, a thorough examination was conducted to examine how we can structure and implement flipped classrooms for language and interpreting training while actively engaging learners. This study adopted a quantitative research method, while complementing it with a qualitative one. The key findings suggest that the significance of the instructor’s role does not dwindle, but his/her role changes to a moderator and a facilitator. Second, we can apply flipped learning to both theory- and practice-oriented modules. Third, students’ integration into the community of inquiry is of significant importance to foster active and higher-order learning. Fourth, cognitive presence and competence can be enhanced through strengthened and integrated teaching and social presences. Well-orchestrated teaching presence stimulates students to find out the problems and voices the convergences and divergences, while fluid social presence facilitates the exchanges of knowledge and the adjustment of solutions, which eventually contributes to consolidating cognitive presence—a key ingredient that enables the application and testing of the solutions and reflection thereon.

Keywords: blended learning, Community of Inquiry, flipped learning, interpreter training, student-centred learning

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Authors: Ahmet Karakuş, Akif Can Kilic, Emre Alptekin

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A growing number of studies have been conducted to determine how well-being may be predicted using well-designed models. It is necessary to investigate the backgrounds of features in order to construct a viable Subjective Well-Being (SWB) model. We have picked the suitable variables from the literature on SWB that are acceptable for real-world data instructions. The goal of this work is to evaluate the model by feeding it with SWB characteristics and then categorizing the stress levels using machine learning methods to see how well it performs on a real dataset. Despite the fact that it is a multiclass classification issue, we have achieved significant metric scores, which may be taken into account for a specific task.

Keywords: machine learning, multiclassification problem, subjective well-being, perceived stress scale

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Authors: Lin Xie, Yang Wang

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An extensive body of research studies suggest that students, teachers, and supervisors can enhance the likelihood of reaching their goals by improving their communication skills. It is highly important to learn how and when to provide different kinds of feedback, e.g. anticipatory, corrective and positive) will gain better result and higher morale. The purpose of this mixed methods research is twofold: 1) To find out what factors affect effective communication among different stakeholders and how these factors affect student learning 2) What are the good practices for improving communication among different stakeholders and improve student achievement. This presentation first begins with an introduction to the recent research on Marshall’s Nonviolent Communication Techniques (NVC), including four important components: observations, feelings, needs, requests. These techniques can be effectively applied at all levels of communication. To develop an in-depth understanding of the relationship among different techniques within, this research collected, compared, and combined qualitative and quantitative data to better improve communication and support student learning.

Keywords: communication, education, language learning, goal achievement, academic success

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Authors: Jolanta Jonak

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Traditional classrooms are challenging for many students, but especially for students that learn differently due to cognitive makeup, learning preferences, or disability. These students often require different teaching approaches and learning opportunities to benefit from learning. Teachers frequently divert to using one teaching approach, the one that matches their own learning style. For instance, teachers that are auditory learners, likely default to providing auditory learning opportunities. However, if a student is a visual learner, he/she may not fully benefit from that teaching style. Based on research, students and their parents’ feedback, large numbers of students are not provided the type of education and types of supports they need in order to be successful in an academic environment. This eventually leads to not learning at an appropriate rate and ultimately leading to skill deficiencies and deficits. Providing varied learning approaches promote high academic and social-emotional growth of all students and it will prevent inaccurate Special Education referrals. Varied learning opportunities can be delivered for all students by providing Differentiated Instruction (DI). This type of instruction allows each student to learn in the most optimal way regardless of learning preferences and cognitive learning profiles. Using Differentiated Instruction will lead to a high level of student engagement and learning. In addition, experiencing success in the classroom, will contribute to increased social emotional wellbeing. Being cognizant of how teaching approaches impact student's learning, school staff can avoid inaccurate perceptions about the students’ learning abilities, unnecessary referrals for special education evaluations, and inaccurate decisions about the presence of a disability. This presentation will illustrate learning differences due to various factors, how to recognize them, and how to address them through Differentiated Instruction.

Keywords: special education, disability, differences, differentiated instruction, social emotional wellbeing

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Authors: Elena F. Ruiz, Marina Vicario, Chadwick Carreto, Rubén Peredo

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One of the main obstacles in Mexico’s engineering programs is math comprehension, especially in the Derivative concept. Due to this, we present a study case that relates Mobile Computing and Classroom Learning in the “Escuela Superior de Cómputo”, based on the Educational model of the Instituto Politécnico Nacional (competence based work and problem solutions) in which we propose apps and activities to teach the concept of the Derivative. M- Learning is emphasized as one of its lines, as the objective is the use of mobile devices running an app that uses its components such as sensors, screen, camera and processing power in classroom work. In this paper, we employed Augmented Reality (ARRoC), based on the good results this technology has had in the field of learning. This proposal was developed using a qualitative research methodology supported by quantitative research. The methodological instruments used on this proposal are: observation, questionnaires, interviews and evaluations. We obtained positive results with a 40% increase using M-Learning, from the 20% increase using traditional means.

Keywords: augmented reality, classroom learning, educational research, mobile computing

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Authors: Arshia Aflaki, Hadis Karimipour, Anik Islam

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Generative Adversarial Attacks (GAAs) threaten critical sectors, ranging from fingerprint recognition to industrial control systems. Existing Deep Learning (DL) algorithms are not robust enough against this kind of cyber-attack. As one of the most critical industries in the world, the power grid is not an exception. In this study, a Deep Reinforcement Learning-based (DRL) framework assisting the DL model to improve the robustness of the model against generative adversarial attacks is proposed. Real-world smart grid stability data, as an IIoT dataset, test our method and improves the classification accuracy of a deep learning model from around 57 percent to 96 percent.

Keywords: generative adversarial attack, deep reinforcement learning, deep learning, IIoT, generative adversarial networks, power system

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Authors: Pia Wetzl

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The learning process of students can be optimized by combining online teaching with face-to-face sessions. So-called blended learning offers extensive flexibility as well as contact opportunities with fellow students and teachers. Furthermore, learning can be individualized and self-regulated. The aim of this article is to investigate which factors are necessary for blended learning to be successful. Semi-structured interviews were conducted with students (N = 60) and lecturers (N = 21) from different disciplines at two German universities. The questions focused on the perception of online, face-to-face and blended learning courses. In addition, questions focused on possible optimization potential and obstacles to practical implementation. The results show that on-site presence is very important for blended learning to be successful. If students do not get to know each other on-site, there is a risk of loneliness during the self-learning phases. This has a negative impact on motivation. From the perspective of the lecturers, the willingness of the students to participate in the sessions on-site is low. Especially when there is no obligation to attend, group work is difficult to implement because the number of students attending is too low. Lecturers would like to see more opportunities from the university and its administration to enforce attendance. In their view, this is the only way to ensure the success of blended learning. In addition, they see the conception of blended learning courses as requiring a great deal of time, which they are not always willing to invest. More incentives are necessary to keep the lecturers motivated to develop engaging teaching material. The study identifies factors that can help teachers conceptualize blended learning. It also provides specific implementation advice and identifies potential impacts. This catalogue has great value for the future-oriented development of courses at universities. Future studies could test its practical use.

Keywords: blended learning, higher education, teachers, student learning, qualitative research

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Authors: Christine K. Fulmer

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Social work education is competency based in nature. There is an expectation that graduates of social work programs throughout the world are to be prepared to practice at a level of competence, which is beneficial to both the well-being of individuals and community. Experiential learning is one way to prepare students for competent practice. The use of Problem-Based Learning (PBL) is a form experiential education that has been successful in a number of disciplines to bridge the gap between the theoretical concepts in the classroom to the real world. PBL aligns with the constructivist theoretical approach to learning, which emphasizes the integration of new knowledge with the beliefs students already hold. In addition, the basic tenants of PBL correspond well with the practice behaviors associated with social work practice including multi-disciplinary collaboration and critical thinking. This paper makes an argument for utilizing PBL in social work education.

Keywords: social work education, problem-based learning, pedagogy, experiential learning, constructivist theoretical approach

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Authors: Bing Xu, Kerry Lee, Jason M. Stephen

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Collaborative learning (CL) has been regarded as a way to facilitate students to gain knowledge and improve social skills. In China, lecturers in higher education institutions have commonly adopted CL in their daily practice. However, such a strategy could not be effective when it is designed and applied in an inappropriate way. Previous research hardly focused on how CL was applied in Chinese universities. This present study aims to gain a deep understanding of how Chinese lecturers design and implement CL tasks. The researchers interviewed ten lecturers from different faculties in various universities in China and usedGroup Learning Activity Instructional Design (GLAID) framework to analyse the data. We found that not all lecturers pay enough attention to eight essential components (proposed by GLAID) when they designed CL tasks, especially the components of Structure and Guidance. Meanwhile, only a small part of lecturers made formative assessment to help students improve learning. We also discuss the strengths and limitations and CL design and further provide suggestions to the lecturers who intend to use CL in class. Research Objectives: The aims of the present research are threefold. We intend to 1) gain a deep understanding of how Chinese lecturers design and implement collaborative learning (CL) tasks, 2) find strengths and limitations of CL design in higher education, and 3) give suggestions about how to improve the design and implement. Research Methods: This research adopted qualitative methods. We applied the semi-structured interview method to interview ten Chinese lecturers about how they designed and implemented CL tasks in their courses. There were 9 questions in the interview protocol focusing on eight components of GLAID. Then, underpinning the GLAID framework, we utilized the coding reliability thematic analysis method to analyse the research data. The coding work was done by two PhD students whose research fields are CL, and the Cohen’s Kappa was 0.772 showing the inter-coder reliability was good. Contribution: Though CL has been commonly adopted in China, few studies have paid attention to the details about how lecturers designed and implemented CL tasks in practice. This research addressed such a gap and found not lecturers were aware of how to design CL and felt it difficult to structure the task and guide the students on collaboration, and further ensure student engagement in CL. In summary, this research advocates for teacher training; otherwise, students may not gain the expected learning outcomes.

Keywords: collaborative learning, higher education, task design, GLAID framework

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Authors: Sharifah Mousli, Sona Taheri, Jiayuan He

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Autism spectrum disorder (ASD) is a complex developmental condition involving persistent difficulties with social communication, restricted interests, and repetitive behavior. The challenges associated with ASD can interfere with an affected individual’s ability to function in social, academic, and employment settings. Although there is no effective medication known to treat ASD, to our best knowledge, early intervention can significantly improve an affected individual’s overall development. Hence, an accurate diagnosis of ASD at an early phase is essential. The use of machine learning approaches improves and speeds up the diagnosis of ASD. In this paper, we focus on the application of unsupervised clustering methods in ASD as a large volume of ASD data generated through hospitals, therapy centers, and mobile applications has no pre-existing labels. We conduct a comparative analysis using seven clustering approaches such as K-means, agglomerative hierarchical, model-based, fuzzy-C-means, affinity propagation, self organizing maps, linear vector quantisation – as well as the recently developed optimization-based clustering (COMSEP-Clust) approach. We evaluate the performances of the clustering methods extensively on real-world ASD datasets encompassing different age groups: toddlers, children, adolescents, and adults. Our experimental results suggest that the COMSEP-Clust approach outperforms the other seven methods in recognizing ASD with well-separated clusters.

Keywords: autism spectrum disorder, clustering, optimization, unsupervised machine learning

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Authors: A. Yacob, N. Hj. Ali, M. H. Yusoff, M. Y. MohdSaman, W. M. A. F. W. Hamzah

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Personalized learning becomes increasingly popular which not is restricted by time, place or any other barriers. This study proposes an analysis of Personalized Learning using Item Response Theory which considers course material difficulty and learner ability. The study investigates twenty undergraduate students at TATI University College, who are taking programming subject. By using the IRT, it was found that, finding the most appropriate problem levels to each student include high and low level test items together is not a problem. Thus, the student abilities can be asses more accurately and fairly. Learners who experience more anxiety will affect a heavier cognitive load and receive lower test scores. Instructors are encouraged to provide a supportive learning environment to enhance learning effectiveness because Cognitive Load Theory concerns the limited capacity of the brain to absorb new information.

Keywords: assessment, item response theory, cognitive load theory, learning, motivation, performance

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Authors: Salvador Antelmo Casanova Valencia

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This study documents the use of the Minecraft Education Edition application to explore immersive game-based learning environments. We analyze the contributions of fourth-year university students who are pursuing a degree in Administrative Computing at the Universidad Michoacana de San Nicolas de Hidalgo. In this study, descriptive data and statistical inference are detailed using a quasi-experimental design using the Wilcoxon test. The instruments will provide data validation. Game-based learning in immersive environments necessarily implies greater student participation and commitment, resulting in the study, motivation, and significant improvements, promoting cooperation and autonomous learning.

Keywords: game-based learning, gamification, higher education, Minecraft

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Authors: Subramaniam Chandran, A. Geetha

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This paper addresses three issues: how to prepare the instructional design for imparting English language skill from inter-disciplinary self-learning material; how the disadvantaged students are benefited from such kind of language skill imparted through m-learning; and how do m-learners perform better than the other learners. This paper examines these issues through an experimental study conducted among the distance learners enrolled in a preparatory program for bachelor’s degree. This program is designed for the disadvantaged learners especially for the school drop-outs to qualify to pursue graduate program through distant education. It also explains how mobile learning helps them to enhance their capacity in learning despite their rural background and other disadvantages. In India, nearly half of the students enrolled in schools do not complete their study. The pursuance of higher education is very low when compared with developed countries. This study finds a significant increase in their learning capacity and mobile learning seems to be a viable alternative where the conventional system could not reach the disadvantaged learners. Improving the English language skill is one of the reasons for such kind of performance. Exercises framed from the relevant self-learning material for enhancing English language skill not only improves language skill but also widens the subject-knowledge. This paper explains these issues out of the study conducted among the disadvantaged learners.

Keywords: English language skill, disadvantaged learners, distance education, m-learning

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Authors: Seyed-Ali Sadegh-Zadeh, Kaveh Kavianpour, Hamed Atashbar, Elham Heidari, Saeed Shiry Ghidary, Amir M. Hajiyavand

Abstract:

Evaluation metrics play a critical role in assessing the performance of machine learning models. In this review paper, we provide a comprehensive overview of performance measurement approaches for machine learning models. For each category, we discuss the most widely used metrics, including their mathematical formulations and interpretation. Additionally, we provide a comparative analysis of performance measurement approaches for metric combinations. Our review paper aims to provide researchers and practitioners with a better understanding of performance measurement approaches and to aid in the selection of appropriate evaluation metrics for their specific applications.

Keywords: evaluation metrics, performance measurement, supervised learning, unsupervised learning, reinforcement learning, model robustness and stability, comparative analysis

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Authors: Annette Cosgrove

Abstract:

The objectives of this research study is to examine the impact of professional development in Technology Enhanced Learning (TEL) and the digitisation of learning in teaching communities across multiple higher education sites in the ATU (Atlantic Technological University *) ( 2020-2025), including the proposal of an evidence based digital teaching model for use in a future pandemic. The research strategy undertaken for this PhD Study is a multi-site study using mixed methods. Qualitative & quantitative methods are being used in the study to collect data. A pilot study was carried out initially , feedback collected and the research instrument was edited to reflect this feedback, before being administered. The purpose of the staff questionnaire is to evaluate the impact of professional development in the area of TEL, and to capture the practitioners views on the perceived impact on their teaching practice in the higher education sector across ATU (West of Ireland – 5 Higher education locations ). The phenomenon being explored is ‘ the impact of professional development in the area of technology enhanced learning and on teaching practice in a higher education institution.’ The research methodology chosen for this study is an Action based Research Study. The researcher has chosen this approach as it is a prime strategy for developing educational theory and enhancing educational practice . This study includes quantitative and qualitative methods to elicit data which will quantify the impact that continuous professional development in the area of digital teaching practice and technologies has on the practitioner’s teaching practice in higher education. The research instruments / data collection tools for this study include a lecturer survey with a targeted TEL Practice group ( Pre and post covid experience) and semi-structured interviews with lecturers.. This research is currently being conducted across the ATU multisite campus and targeting Higher education lecturers that have completed formal CPD in the area of digital teaching. ATU, a west of Ireland university is the focus of the study , The research questionnaire has been deployed, with 75 respondents to date across the ATU - the primary questionnaire and semi- formal interviews are ongoing currently – the purpose being to evaluate the impact of formal professional development in the area of TEL and its perceived impact on the practitioners teaching practice in the area of digital teaching and learning . This paper will present initial findings, reflections and data from this ongoing research study.

Keywords: TEL, DTL, digital teaching, digital assessment

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