Search results for: dictionary learning

Authors: Alexander Huang

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Online learning sessions have become an indispensable complement to in-classroom-learning sessions in the past two years due to the emergence of Covid-19. Due to social distance requirements, many courses and interaction-intensive sessions, ranging from music classes to debate camps, are online. However, online learning imposes a significant challenge for engaging students effectively during the learning sessions. To resolve this problem, Project PWR, a non-profit organization formed by high school students, developed an online kid-teach-kid learning environment to boost students' learning interests and further improve students’ engagement during online learning. Fundamentally, the kid-teach-kid learning model creates an affinity space to form learning groups, where like-minded peers can learn and teach their interests. The role of the teacher can also help a kid identify the instructional task and set the rules and procedures for the activities. The approach also structures initial discussions to reveal a range of ideas, similar experiences, thinking processes, language use, and lower student-to-teacher ratio, which become enriched online learning experiences for upcoming lessons. In such a manner, a kid can practice both the teacher role and the student role to accumulate experiences on how to convey ideas and questions over the online session more efficiently and effectively. In this research work, we conducted two case studies involving a 3D-Design course and a Speech and Debate course taught by high-school kids. Through Project PWR, a kid first needs to design the course syllabus based on a provided template to become a student-teacher. Then, the Project PWR academic committee evaluates the syllabus and offers comments and suggestions for changes. Upon the approval of a syllabus, an experienced and voluntarily adult mentor is assigned to interview the student-teacher and monitor the lectures' progress. Student-teachers construct a comprehensive final evaluation for their students, which they grade at the end of the course. Moreover, each course requires conducting midterm and final evaluations through a set of surveyed replies provided by students to assess the student-teacher’s performance. The uniqueness of Project PWR lies in its established kid-teach-kids affinity space. Our research results showed that Project PWR could create a closed-loop system where a student can help a teacher improve and vice versa, thus improving the overall students’ engagement. As a result, Project PWR’s approach can train teachers and students to become better online learners and give them a solid understanding of what to prepare for and what to expect from future online classes. The kid-teach-kid learning model can significantly improve students' engagement in the online courses through the Project PWR to effectively supplement the traditional teacher-centric model that the Covid-19 pandemic has impacted substantially. Project PWR enables kids to share their interests and bond with one another, making the online learning environment effective and promoting positive and effective personal online one-on-one interactions.

Keywords: kid-teach-kid, affinity space, online learning, engagement, student-teacher

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Authors: Mohd Khairezan Rahmat

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Since the impetus of becoming a develop country by the year 2020, the Malaysian government have been proactive in strengthening the integration of ICT into the national educational system. Teacher-education programs have the responsibility to prepare the nation future teachers by instilling in them the desire, confidence, and ability to fully utilized the potential of ICT into their instruction process. In an effort to fulfill this responsibility, teacher-education program are beginning to create alternatives means for preparing cutting-edge teachers. One of the alternatives is the student’s learning portal. In line with this mission, this study investigates the Faculty of Education, University Teknologi MARA (UiTM) pre-service teachers’ perception of usefulness, attitude, and ability toward the usage of the university learning portal, known as iLearn. The study also aimed to predict factors that might hinder the pre-service teachers’ decision to used iLearn as their platform in learning. The Structural Equation Modeling (SEM), was employed in analyzed the survey data. The suggested findings informed that pre-service teacher’s successful integration of the iLearn was highly influenced by their perception of usefulness of the system. The findings also suggested that the more familiar the pre-service teacher with the iLearn, the more possibility they will use the system. In light of similar study, the present findings hope to highlight the important to understand the user’s perception toward any proposed technology.

Keywords: e-learning, prediction factors, pre-service teacher, structural equation modeling (SEM)

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Authors: Claire Griffin

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Recent years have witnessed a surge in the use of innovative teaching approaches to support student engagement and higher-order learning within higher education. This paper seeks to explore the use of collaborative, interactive teaching and learning strategies to support student engagement in a final year undergraduate Developmental Psychology module. In particular, the use of the jigsaw method, in-class presentations and online discussion fora were adopted in a ‘lectorial’ style teaching approach, aimed at scaffolding learning, fostering social interdependence and supporting various levels of student engagement in higher education. Using the ‘Student Course Engagement Questionnaire’, the impact of such teaching strategies on students’ college classroom experience was measured, with additional qualitative student feedback gathered. Results illustrate the positive impact of the teaching methodologies on students’ levels of engagement, with positive implications emerging across the four engagement factors: skills engagement, emotional engagement, participation/interaction engagement and performance engagement. Thematic analysis on students’ qualitative comments also provided greater insight into the positive impact of the ‘lectorial’ teaching approach on students’ classroom experience within higher level education. Implications of the findings are presented in terms of informing effective teaching practices within higher education. Additional avenues for future research and strategy usage will also be discussed, in light of evolving practice and cutting edge literature within the field.

Keywords: learning, higher education, scaffolding, student engagement

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Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

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In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: ANPR, CS, CNN, deep learning, NPL

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Authors: Shubham Bhonde, Nekzad Doctor, Shashwat Gawande

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For the use and the import of Keys and ID Transmitter as well as Body Control Modules with radio transmission in a lot of countries, homologation is required. Final deliverables in homologation of the product are certificates. In considering the world of homologation, there are approximately 200 certificates per product, with most of the certificates in local languages. It is challenging to manually investigate each certificate and extract relevant data from the certificate, such as expiry date, approval date, etc. It is most important to get accurate data from the certificate as inaccuracy may lead to missing re-homologation of certificates that will result in an incompliance situation. There is a scope of automation in reading the certificate data in the field of homologation. We are using deep learning as a tool for automation. We have first trained a model using machine learning by providing all country's basic data. We have trained this model only once. We trained the model by feeding pdf and jpg files using the ETL process. Eventually, that trained model will give more accurate results later. As an outcome, we will get the expiry date and approval date of the certificate with a single click. This will eventually help to implement automation features on a broader level in the database where certificates are stored. This automation will help to minimize human error to almost negligible.

Keywords: homologation, re-homologation, data science, deep learning, machine learning, ETL (extract transform loading)

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Authors: Mahmoud I. Syam, Osama K. El-Hafy

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With lack of student motivation, there will be a little or no real learning in the class and this directly effects student achievement and test scores. Some students are naturally motivated to learn, but many students are not motivated, they do care little about learning and need their instructors to motivate them. Thus, motivating students is part of the instructor’s job. It’s a tough task to motivate students and make them have more attention and enthusiasm. As a part of this research, a questionnaire has been distributed among a sample of 155 students out of 1502 students from Foundation Program at Qatar University. The questionnaire helped us to determine some methods to motivate the students and encourage them to study such as variety of teaching activities, encouraging students to participate during the lectures, creating intense competition between the students, using instructional technology, not using grades as a threat and respecting the students and treating them in a good manner. Accordingly, some hypotheses are tested and some recommendations are presented.

Keywords: learning, motivating, student, teacher, testing hypotheses

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Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

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Industrial end-users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both machine learning and first principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed by breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems, and humans -including asset maintenance operations- in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: degradation models, ageing, anomaly detection, soft sensor, incremental learning

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Authors: Shiyin He, Zheng Huang

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In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable.

Keywords: cell detection, cell recognition, deep learning, Mask-RCNN, ResNet

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Authors: Ghiwa Dandach

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By challenging the anti-failure bias and contributing to the theoretical territory of the attribution theory, this thesis develops a comprehensive process for entrepreneurial learning from failure. The practical implication of the findings suggests assisting entrepreneurs (current, failing, and nascent) in effectively anticipating and reflecting upon failure. Additionally, the process is suggested to enhance the level of institutional and private (accelerators and financers) support provided to entrepreneurs, the implications of which may improve future opportunities for entrepreneurial success. Henceforth, exploring learning from failure is argued to impact the potential survival of future ventures, subsequently revitalizing the economic contribution of entrepreneurship. This learning process can be enhanced with the cognitive development of causal ascriptions for failure, which eventually impacts learning outcomes. However, the mechanism with which entrepreneurs make sense of failure, reflect on the journey, and transform experience into knowledge is still under-researched. More specifically, the cognitive process of failure attribution is under-explored, majorly in the context of developing economies, calling for a more insightful understanding on how entrepreneurs ascribe failure. Responding to the call for more thorough research in such cultural contexts, this study expands the understanding of the dimensions of failure attributions as perceived by entrepreneurs and the impact of these dimensions on learning outcomes in the Lebanese context. The research adopted the exploratory interpretivism paradigm and collected data from interviews with industry experts first, followed by narratives of entrepreneurs using the qualitative multimethod approach. The holistic and categorical content analysis of narratives, preceded by the thematic analysis of interviews, unveiled how entrepreneurs ascribe failure by developing minor and major dimensions of each failure attribution. The findings have also revealed how each dimension impacts the learning from failure when accompanied by emotional resilience. The thesis concludes that exploring in-depth the dimensions of failure attributions significantly determines the level of learning generated. They are moving beyond the simple categorisation of ascriptions as primary internal or external unveiled how learning may occur with each attribution at the individual, venture, and ecosystem levels. This has further accentuated that a major internal attribution of failure combined with a minor external attribution generated the highest levels of transformative and double-loop learning, emphasizing the role of personal blame and responsibility on enhancing learning outcomes.

Keywords: attribution, entrepreneurship, reflection, sense-making, emotions, learning outcomes, failure, exit

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Authors: Ijaz Ahmed, Anwar Zada, Muhammad Waqas, M. U. Ashraf

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We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at √s = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach

Keywords: top tagger, multivariate, deep learning, LHC, single top

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Authors: Alhusein Almahjoub, Dongyu Qiu

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This paper deals with the random access procedure in next-generation networks and presents the solution to reduce total service time (TST) which is one of the most important performance metrics in current and future internet of things (IoT) based networks. The proposed solution focuses on the calculation of optimal transmission probability which maximizes the success probability and reduces TST. It uses the information of several idle preambles in every time slot, and based on it, it estimates the number of backlogged IoT devices using Naïve Bayes estimation which is a type of supervised learning in the machine learning domain. The estimation of backlogged devices is necessary since optimal transmission probability depends on it and the eNodeB does not have information about it. The simulations are carried out in MATLAB which verify that the proposed solution gives excellent performance.

Keywords: random access, LTE/LTE-A, 5G, machine learning, Naïve Bayes estimation

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Authors: Xiao Zhou, Jianlin Cheng

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A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.

Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining

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Authors: Gabriela Teodorescu, Ioana Daniela Dulama

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In an ever-changing society, the education system needs to constantly evolve to meet market demands. During its 30 years of existence, Valahia University of Targoviste (VUT) tried to offer its students a series of teaching-learning schemes that would prepare them for a remarkable career. In VUT, the achievement of performance through innovation can be analyzed by reference to several key indicators (i.e., university climate, university resources, and innovative methods applied to classes), but it is possible to differentiate between activities in the classic format: participate to courses; interactive seminars and tutorials; laboratories, workshops, project-based learning; entrepreneurial activities, through simulated enterprises; mentoring activities. Thus, VUT has implemented over time a series of schemes and projects based on innovation and entrepreneurship, and in this paper, some of them will be briefly presented. All these schemes were implemented by facilitating an effective dialog with students and the opportunity to listen to their views at all levels of the University and in all fields of study, as well as by developing a partnership with students to set out priority areas. VUT demonstrates innovation and entrepreneurial capacity through its new activities for higher education, which will attract more partnerships and projects dedicated to students.

Keywords: Romania, project-based learning, entrepreneurial activities, simulated enterprises

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Authors: Sultan Ahmed Arishi

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The objective of the study was to investigate the attitude of the Saudi students attending the English Language programmes of the Royal Commission for Jubail towards using CALL, as well as to discover whether computer-assisted teaching is useful and valuable for students in learning English. Data were collected with the help of interviews and survey questionnaires. The outcomes of the investigation showed that students had a positive attitude towards CALL. Moreover, the listening skills of the students had the most substantial effect on students learning English through CALL. Unexpectedly, the teaching staff, equipment, curriculum, or even a student's poor English background was a distinct barrier that attributed to any weaknesses of using CALL, or in other words, all these factors were of a similar attitude.

Keywords: CALL, teaching aids, teaching technology, teaching English with technology, teaching English in Saudi Arabia

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Authors: Seoi Lee, Dongjoo Chin, Heewon Kim

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Gamification refers to a technique that applies game elements to non-gaming elements, such as education and exercise, to make people more engaged in these behaviors. The purpose of this study was to identify effective elements in gamification for changing human behaviors. In order to accomplish this purpose, a survey based on learning theory was developed, especially for assessing antecedents and consequences of behaviors, and 8 popular and 8 unpopular games were selected for comparison. A total of 407 adult males and females were recruited via crowdsourcing Internet marketplace and completed the survey, which consisted of 19 questions for antecedent and 14 questions for consequences. Results showed no significant differences in consequence questions between popular and unpopular games. For antecedent questions, popular games are superior to unpopular games in character customization, play type selection, a sense of belonging, patch update cycle, and influence or dominance. This study is significant in that it reveals the elements of gamification based on learning theory. Future studies need to empirically validate whether these factors affect behavioral change.

Keywords: gamification, learning theory, antecedent, consequence, behavior change, behaviorism

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Authors: Sreekanth Reddy Ballarapu

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The perception of the whole process of teaching and learning is undergoing a drastic and radical change. More and more student-centered, pragmatic, and flexible approaches are gradually replacing teacher-centered lecturing and structural-syllabus instruction. The issue of teaching English as a Foreign language is no exception in this regard. The traditional Present-Practice-Produce (P-P-P) method of teaching English is overtaken by Task-Based Teaching which is a subsidiary branch of Communicative Language Teaching. At this juncture this article strongly tries to convey that - Task-based learning, has an advantage over other traditional methods of teaching. All teachers of English must try to customize their texts into productive tasks, apply them, and evaluate the students as well as themselves. Task Based Learning is a double edged tool which can enhance the performance of both the teacher and the taught. The sample for this case study is a class of 35 students from Semester III - Network branch at TVTC College of Technology, Adhum - Kingdom of Saudi Arabia. The students are high school passed out and aged between 19-21years.For the present study the prescribed textbook Technical English 1 by David Bonamy was used and a number of language tasks were chalked out during the pre- task stage and the learners were made to participate voluntarily and actively. The Action Research methodology was adopted within the dual framework of Communicative Language Teaching and Task-Based Learning. The different tools such as questionnaires, feedback and interviews were used to collect data. This study provides information about various techniques of Communicative Language Teaching and Task Based Learning and focuses primarily on the advantages of using a Task Based Learning approach. This article presents in detail the objectives of the study, the planning and implementation of the action research, the challenges encountered during the execution of the plan, and the pedagogical outcome of this project. These research findings serve two purposes: first, it evaluates the effectiveness of Task Based Learning and, second, it empowers the teacher's professionalism in designing and implementing the tasks. In the end, the possibility of scope for further research is presented in brief.

Keywords: action research, communicative language teaching, task based learning, perception

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Authors: Samuel Mwangi, Josephine K. Mule

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Access control as a security technique regulates who or what can access resources. It is a fundamental concept in security that minimizes risks to the institutions that use access control. Regulating access to institutions of higher learning is key to ensure only authorized personnel and students are allowed into the institutions. The use of biometrics has been criticized due to the setup and maintenance costs, hygiene concerns, and trepidations regarding data privacy, among other apprehensions. Facial recognition is arguably a fast and accurate way of validating identity in order to guard protected areas. It guarantees that only authorized individuals gain access to secure locations while requiring far less personal information whilst providing an additional layer of security beyond keys, fobs, or identity cards. This exploratory study sought to investigate the use of facial recognition in controlling access in institutions of higher learning in Kenya. The sample population was drawn from both private and public higher learning institutions. The data is based on responses from staff and students. Questionnaires were used for data collection and follow up interviews conducted to understand responses from the questionnaires. 80% of the sampled population indicated that there were many security breaches by unauthorized people, with some resulting in terror attacks. These security breaches were attributed to stolen identity cases, where staff or student identity cards were stolen and used by criminals to access the institutions. These unauthorized accesses have resulted in losses to the institutions, including reputational damages. The findings indicate that security breaches are a major problem in institutions of higher learning in Kenya. Consequently, access control would be beneficial if employed to curb security breaches. We suggest the use of facial recognition technology, given its uniqueness in identifying users and its non-repudiation capabilities.

Keywords: facial recognition, access control, technology, learning

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Authors: Natalia Sáez, Dina Shulfman

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There is an important research gap regarding accessible pedagogical techniques for teaching foreign languages to adults with visual impairments. English as a foreign language (EFL), in particular, is needed in many countries to expand occupational opportunities and improve living standards. Within EFL research, teaching and learning prepositions have only recently gained momentum, considering that they constitute one of the most difficult structures to learn in a foreign language and are fundamental for communicating about spatial relations in the world, both on the physical and imaginary levels. Learning to use prepositions would not only facilitate communication when referring to the surrounding tangible environment but also when conveying ideas about abstract topics (e.g., justice, love, society), for which students’ sociocultural knowledge about space could play an important role. By potentiating visually impaired students’ ability to construe mental spatial imagery, this study made efforts to explore pedagogical techniques that cater to their strengths, helping them create new worlds by welcoming and expanding their sociocultural funds of knowledge as they learn to use English prepositions. Fifteen visually impaired adults living in Chile participated in the study. Their first language was Spanish, and they were learning English at the intermediate level of proficiency in an EFL workshop at La Biblioteca Central para Ciegos (The Central Library for the Blind). Within this workshop, a series of activities and interviews were designed and implemented with the intention of uncovering students’ spatial funds of knowledge when learning literal/physical uses of three English prepositions, namely “in,” “at,” and “on”. The activities and interviews also explored whether students used their original spatial funds of knowledge when learning metaphorical uses of these prepositions and if their use of spatial imagery changed throughout the learning activities. Over the course of approximately half a year, it soon became clear that the students construed mental images of space when learning both literal/physical and metaphorical uses of these prepositions. This research could inform a new approach to inclusive language education using pedagogical methods that are relevant and accessible to students with visual impairments.

Keywords: EFL, funds of knowledge, prepositions, spatial cognition, visually impaired students

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Authors: Mohammad Hadi Mahmoodi, Soroor Zekrati

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The aim of this study was to investigate the relationship between hemispheric dominance and intonation learning of Iranian EFL students. In order to gain this goal, 52 female students from three levels of beginner, elementary and intermediate in Paradise Institute, and 18 male university students at Bu-Ali Sina University constituted the sample. In order to assist students learn the correct way of applying intonation to their everyday speech, the study proposed an interactive approach and provided students with visual aid through which they were able to see the intonation pattern on computer screen using 'Speech Analyzer' software. This software was also used to record subjects’ voice and compare them with the original intonation pattern. Edinburg Handedness Questionnaire (EHD), which ranges from –100 for strong left-handedness to +100 for strong right-handedness was used to indicate the hemispheric dominance of each student. The result of an independent sample t-test indicated that girls learned intonation pattern better than boys, and that right brained students significantly outperformed the left brained ones. Using one-way ANOVA, a significant difference between three proficiency levels was also found. The posthoc Scheffer test showed that the exact difference was between intermediate and elementary, and intermediate and beginner levels, but no significant difference was observed between elementary and beginner levels. The findings of the study might provide researchers with some helpful implications and useful directions for future investigation into the domain of the relationship between mind and second language learning.

Keywords: intonation, hemispheric dominance, visual aid, language learning, second language learning

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Authors: Xiao Liulingzi

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It is important for using multi-sensory teaching in music learning. This article aims to provide knowledge in multi-sensory learning and teaching music in primary school. For primary school students, in addition to the training of basic knowledge and skills of music, students' sense of participation and creativity in music class are the key requirements, especially the flexibility and dynamics in music class, so that students can integrate into music and feel the music. The article explains the multi-sensory sense in music learning, the differences between multi-sensory music teaching and traditional music teaching, and music multi-sensory teaching in primary schools in China.

Keywords: multi-sensory, teaching practice, primary music classroom, China

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Authors: Jodie Lee, Lorena Chan, Esther Tong

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Hong Kong students face a unique learning environment. Starting from the 2010/2011 school year, The Education Bureau (EDB) of the Government of the Hong Kong Special Administrative Region implemented the fine-tuned Medium of Instruction (MOI) arrangements for secondary schools. Since then, secondary schools in Hong Kong have been given the flexibility to decide the most appropriate MOI arrangements for their schools and under the new academic structure for senior secondary education, particularly on the compulsory part of the mathematics curriculum. In 2019, Hong Kong Diploma of Secondary Education Examination (HKDSE), over 40% of school day candidates attempted the Mathematics Compulsory Part examination in the Chinese version while the rest took the English version. Moreover, only 14.38% of candidates sat for one of the extended Mathematics modules. This results in a serious of intricate issues to students’ learning in post-secondary education programmes. It is worth to note that when students further pursue to an higher education in Hong Kong or even oversea, they may facing substantial difficulties in transiting learning from learning mathematics in their mother tongue in Chinese-medium instruction (CMI) secondary schools to an English-medium learning environment. Some students understood the mathematics concepts were found to fail to fulfill the course requirements at college or university due to their learning experience in secondary study at CMI. They are particularly weak in comprehending the mathematics questions when they are doing their assessment or attempting the test/examination. A government funded project was conducted with the aims of providing integrated learning context and language support to students with a lower level of numeracy and/or with CMI learning experience. By introducing this “integrated concept + language mapping approach”, students can cope with the learning challenges in the compulsory English-medium mathematics and statistics subjects in their tertiary education. Ultimately, in the hope that students can enhance their mathematical ability, analytical skills, and numerical sense for their lifelong learning. The “Concept + Language Mapping “(CLM) approach was adopted and tried out in the bridging courses for students with a lower level of numeracy and/or with CMI learning experiences. At the beginning of each class, a pre-test was conducted, and class time was then devoted to introducing the concepts by CLM approach. For each concept, the key thematic items and their different semantic relations are presented using graphics and animations via the CLM approach. At the end of each class, a post-test was conducted. Quantitative data analysis was performed to study the effect on students’ learning via the CLM approach. Stakeholders' feedbacks were collected to estimate the effectiveness of the CLM approach in facilitating both content and language learning. The results based on both students’ and lecturers’ feedback indicated positive outcomes on adopting the CLM approach to enhance the mathematical ability and analytical skills of CMI students.

Keywords: mathematics, Concept+Language Mapping, level of numeracy, medium of instruction

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Authors: Edward K. Boahen, Brunel E. Bouya-Moko, Changda Wang

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The major threat to online social network (OSN) users is account compromisation. Spammers now spread malicious messages by exploiting the trust relationship established between account owners and their friends. The challenge in detecting a compromised account by service providers is validating the trusted relationship established between the account owners, their friends, and the spammers. Another challenge is the increase in required human interaction with the feature selection. Research available on supervised learning (machine learning) has limitations with the feature selection and accounts that cannot be profiled, like application programming interface (API). Therefore, this paper discusses the various behaviours of the OSN users and the current approaches in detecting a compromised OSN account, emphasizing its limitations and challenges. We propose a deep learning approach that addresses and resolve the constraints faced by the previous schemes. We detailed our proposed optimized nonsymmetric deep auto-encoder (OPT_NDAE) for unsupervised feature learning, which reduces the required human interaction levels in the selection and extraction of features. We evaluated our proposed classifier using the NSL-KDD and KDDCUP'99 datasets in a graphical user interface enabled Weka application. The results obtained indicate that our proposed approach outperformed most of the traditional schemes in OSN compromised account detection with an accuracy rate of 99.86%.

Keywords: computer security, network security, online social network, account compromisation

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Authors: Esther Pearl Palmer

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Driven by the increased pressure on Higher Education Institutions (HEIs) to produce work-ready graduates for the modern world of work, this study reflects on triad teaching and learning practices to increase student engagement and employability. In the South African higher education context, the employability of graduates is imperative in strengthening the country’s economy and in increasing competitiveness. Within this context, the field of Human Resource Management (HRM) calls for innovative methods and approaches to teaching and learning and assessing the skills and competencies of graduates to render them employable. Digital competency in Human Resource Information Systems (HRIS) is an important component and prerequisite for employment in HRM. The purpose of this research is to reflect on the subject HRIS developed by lecturers at the Central University of Technology, Free State (CUT), with the intention to actively engage students in real-world learning activities and increase their employability. The Enrichment Triad Model (ETM) was used as theoretical framework to develop the subject as it supports a triad teaching and learning approach to education. It is, furthermore, an inter-structured model that supports collaboration between industry, academics and students. The study follows a mixed-method approach to reflect on the learning experiences of the industry, academics and students in the subject field over the past three years. This paper is a work in progress and seeks to broaden the scope of extant studies about student engagement in work-related learning to increase employability. Based on the ETM as theoretical framework and pedagogical practice, this paper proposes that following a triad teaching and learning approach will increase work-related skills of students. Findings from the study show that students, academics and industry alike regard educational opportunities that incorporate active learning experiences with the world of work enhances student engagement in learning and renders them more employable.

Keywords: digital competence, enriched triad model, human resource information systems, student engagement, triad pedagogy.

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Authors: Zoja Raud, Valery Vodovozov

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The paper focuses on the distance laboratory organisation for training the electrical engineering staff and students in the fields of electrical drive and power electronics. To support online knowledge acquisition and professional enhancement, new challenges in remote education based on an active learning approach with self-assessment have been emerged by the authors. Following the literature review and explanation of the improved assessment methodology, the concept and technological basis of the labs arrangement are presented. To decrease the gap between the distance study of the up-to-date equipment and other educational activities in electrical engineering, the improvements in the following-up the learners’ progress and feedback composition are introduced. An authoring methodology that helps to personalise knowledge acquisition and enlarge Web-based possibilities is described. Educational management based on self-assessment is discussed.

Keywords: advanced training, active learning, distance learning, electrical engineering, remote laboratory, self-assessment

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Authors: Shatha Ghareeb, Rawaa Al-Jumeily, Thar Baker

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In Abu Dhabi, there are many different education curriculums where sector of private schools and quality assurance is supervising many private schools in Abu Dhabi for many nationalities. As there are many different education curriculums in Abu Dhabi to meet expats’ needs, there are different requirements for registration and success. In addition, there are different age groups for starting education in each curriculum. In fact, each curriculum has a different number of years, assessment techniques, reassessment rules, and exam boards. Currently, students that transfer curriculums are not being placed in the right year group due to different start and end dates of each academic year and their date of birth for each year group is different for each curriculum and as a result, we find students that are either younger or older for that year group which therefore creates gaps in their learning and performance. In addition, there is not a way of storing student data throughout their academic journey so that schools can track the student learning process. In this paper, we propose to develop a computational framework applicable in multicultural countries such as UAE in which multi-education systems are implemented. The ultimate goal is to use cloud and fog computing technology integrated with Artificial Intelligence techniques of Machine Learning to aid in a smooth transition when assigning students to their year groups, and provide leveling and differentiation information of students who relocate from a particular education curriculum to another, whilst also having the ability to store and access student data from anywhere throughout their academic journey.

Keywords: admissions, algorithms, cloud computing, differentiation, fog computing, levelling, machine learning

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Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

Abstract:

Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

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Authors: Felix Oromena Egara, Moeketsi Mosia

Abstract:

The study aimed to evaluate the impact of blended learning on mathematics retention among secondary school students. Conducted in the Isoko North Local Government Area of Delta State, Nigeria, the research involved 1,235 senior class one (SS 1) students. Employing a non-equivalent control group pre-test-post-test quasi-experimental design, a sample of 70 students was selected from two secondary schools with ICT facilities through purposive sampling. Random allocation of students into experimental and control groups was achieved through balloting within each selected school. The investigation included three assessment points: pre-Mathematics Achievement Test (MAT), post-MAT, and post-post-MAT (retention), administered systematically by the researchers. Data collection utilized the established MAT instrument, which demonstrated a high reliability score of 0.86. Statistical analysis was conducted using the Statistical Package for Social Sciences (SPSS) version 28, with mean and standard deviation addressing study questions and analysis of covariance scrutinizing hypotheses at a significance level of .05. Results revealed significantly greater improvements in mathematics retention scores among students exposed to blended learning compared to those instructed through conventional methods. Moreover, noticeable differences in mean retention scores were observed, with male students in the blended learning group exhibiting notably higher performance. Based on these findings, recommendations were made, advocating for mathematics educators to integrate blended learning, particularly in geometry teaching, to enhance students’ retention of mathematical concepts.

Keywords: blended learning, flipped classroom model, secondary school students, station rotation model

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Authors: Rafael Dias Silva

Abstract:

The school is a social institution that should promote learning situations that remain throughout life. Based on this, the teaching activities promoted in museum spaces can represent an educational strategy that contributes to the learning process in a more meaningful way. This article systematizes a series of elements that guide the use of these spaces for the scientific literacy of deaf students and as experiences of this nature are favorable for the school development through the concept of the circularity. The methodology for the didactic use of these spaces of non-formal education is one of the reflections developed in this study and how such environments can contribute to the learning in the classroom. To develop in the student the idea of ​​association making him create connections with the curricular proposal and notice how the proposed activity is articulated. It is in our interest that the experience lived in the museum be shared collaborating for the construction of a scientific literacy and cultural identity through the research.

Keywords: accessibility in museums, Brazilian sign language, deaf students, teacher training

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Authors: David Pham, Yongfeng Zhang

Abstract:

Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

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Authors: Ashish Dhamaniya, Vineet Jain, Rajesh Chouhan

Abstract:

Traffic management is one of the gigantic issue in most of the urban roads in al-most all metropolitan cities in India. Speed is one of the critical traffic parameters for effective Intelligent Transportation System (ITS) implementation as it decides the arrival rate of vehicles on an intersection which are majorly the point of con-gestions. The study aimed to leverage Machine Learning (ML) models to produce precise predictions of speed on urban roadway links. The research objective was to assess how categorized traffic volume and road width, serving as variables, in-fluence speed prediction. Four tree-based regression models namely: Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Extreme Gradient Boost (XGB)are employed for this purpose. The models' performances were validated using test data, and the results demonstrate that Random Forest surpasses other machine learning techniques and a conventional utility theory-based model in speed prediction. The study is useful for managing the urban roadway network performance under mixed traffic conditions and effective implementation of ITS.

Keywords: stream speed, urban roads, machine learning, traffic flow

Procedia PDF Downloads 71