Search results for: higher learning institutions

Authors: Abdulmutallib U. Baraya, Aishatu M. Chadi, Zainab A. Aliyu, Agatha Samson

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Learning Resources is the field of study that investigates the process of analyzing, designing, developing, implementing, and evaluating learning materials, learners, and the learning process in order to improve teaching and learning in university-level education essential for empowering students and various sectors of Nigeria’s economy to succeed in a fast-changing global economy. Innovation in the information age of the 21st century is the use of educational technologies in the classroom for instructional delivery, it involves the use of appropriate educational technologies like smart boards, computers, projectors and other projected materials to facilitate learning and improve performance. The study examined learning resources as determinants for improving the teaching and learning process in Abubakar Tafawa Balewa University (ATBU), Bauchi, Bauchi state of Nigeria. Three objectives, three research questions and three null hypotheses guided the study. The study adopted a Survey research design. The population of the study was 880 lecturers. A sample of 260 was obtained using the research advisor table for determining sampling, and 250 from the sample was proportionately selected from the seven faculties. The instrument used for data collection was a structured questionnaire. The instrument was subjected to validation by two experts. The reliability of the instrument stood at 0.81, which is reliable. The researchers, assisted by six research assistants, distributed and collected the questionnaire with a 75% return rate. Data were analyzed using mean and standard deviation to answer the research questions, whereas simple linear regression was used to test the null hypotheses at a 0.05 level of significance. The findings revealed that physical facilities and digital technology tools significantly improved the teaching and learning process. Also, consumables, supplies and equipment do not significantly improve the teaching and learning process in the faculties. It was recommended that lecturers in the various faculties should strengthen and sustain the use of digital technology tools, and there is a need to strive and continue to properly maintain the available physical facilities. Also, the university management should, as a matter of priority, continue to adequately fund and upgrade equipment, consumables and supplies frequently to enhance the effectiveness of the teaching and learning process.

Keywords: education, facilities, learning-resources, technology-tools

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Authors: Yasaswi Palagummi, Sareh Rowlands

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Generalised Zero-Shot Learning, often known as GZSL, is an advanced variant of zero-shot learning in which the samples in the unseen category may be either seen or unseen. GZSL methods typically have a bias towards the seen classes because they learn a model to perform recognition for both the seen and unseen classes using data samples from the seen classes. This frequently leads to the misclassification of data from the unseen classes into the seen classes, making the task of GZSL more challenging. In this work of ours, to solve the GZSL problem, we propose an approach leveraging the Shifted Window based Self-Attention in the Swin Transformer (Swin-GZSL) to work in the inductive GSZL problem setting. We run experiments on three popular benchmark datasets: CUB, SUN, and AWA2, which are specifically used for ZSL and its other variants. The results show that our model based on Swin Transformer has achieved state-of-the-art harmonic mean for two datasets -AWA2 and SUN and near-state-of-the-art for the other dataset - CUB. More importantly, this technique has a linear computational complexity, which reduces training time significantly. We have also observed less bias than most of the existing GZSL models.

Keywords: generalised, zero-shot learning, inductive learning, shifted-window attention, Swin transformer, vision transformer

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Authors: Isiya Shinkafi Salihu

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Open education resources (OER) are open learning materials in different formats, course content and context to support learning globally. This study investigated the level of awareness of Hospitality and Tourism OER among students in the Department of Tourism and Hotel Management in a University. Specifically, it investigated students’ awareness, use and accessibility of OER in learning. The research design method used was the quantitative approach, using an online questionnaire. The thesis research shows that respondents frequently use OER but with little knowledge of the content and context of the material. Most of the respondents’ have little knowledge about the concept even though they use it. Information and communication technologies are tools for information gathering, social networking and knowledge sharing and transfer. OER are open education materials accessible online such as curriculum, maps, course materials, and videos that users create, adapt, reuse for learning and research. Few of the respondents that used OER in learning faced some challenges such as high cost of data, poor connectivity and lack of proper guidance. The results suggest a lack of awareness of OER among students in the faculty of tourism and the need for support from the teachers in the utilization of OER. The thesis also reveals that some of the international students are accessing the internet as beginners in their studies which require guidance. The research, however, recommends that further studies should be conducted to other faculties.

Keywords: creative commons, open education resources, open licenses, information and communication technology

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Authors: Mohan Bikram Thapa

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Despite the successful implementation of community forestry program, a number of pros and cons have been raised on Terai community forestry in the case of lowland locally called Terai region of Nepal, which climatically belongs to tropical humid and possessed high-quality forests in terms of ecology and economy. The study aims to investigate the local pricing strategy of forest products and its impacts on equitable forest benefits sharing, the collection of community fund and carrying out livelihood improvement activities. The study was carried out on six community forests revealed that local people have substantially benefited from the community forests. However, being the region is heterogeneous by socio-economic conditions and forest resources have higher economic potential, the decision of low pricing strategy made by the local people have created inequality problems while sharing the forest benefits, and poorly contributed to community fund collection and consequently carrying out limited activities of livelihood improvement. The paper argued that the decision of low pricing strategy of forest products is counterproductive to promote the equitable benefit-sharing in the areas of heterogeneous socio-economic conditions with high-value forests. The low pricing strategy has been increasing accessibility of better off households at a higher rate than poor, as such households always have the higher affording capacity. It is also defective to increase the community fund and carry out activities of livelihood improvement effectively. The study concluded that unilateral decentralized forest policy and decision-making autonomy to the local people seems questionable unless their decision-making capacities are enriched sufficiently. Therefore, it is recommended that empowerments of decision-making capacity of local people and their respective institutions together with policy and program formulation are prerequisite for efficient and equitable community forest management and its long-term sustainability.

Keywords: benefit sharing, community forest, livelihood, pricing mechanism, Nepal

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Authors: Elahesadat Emrani

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The role of psychology in language teaching has gained significant recognition and importance in recent years. This article explores the intersection of psychology and language teaching and highlights the profound impact that psychological principles and theories have on language learning and instruction. It discusses how an understanding of learners' cognitive processes, motivations, and affective factors can inform instructional strategies, curriculum design, and assessment practices. Additionally, the article sheds light on the importance of considering individual differences and diverse learning styles within the psychological framework of language teaching. This article emphasizes the significance of incorporating psychological insights into language classrooms to create a supportive and effective learning environment. Furthermore, it acknowledges the role of psychology in fostering learner autonomy, enhancing learner motivation, promoting effective communication, and facilitating language acquisition. Overall, this article underscores the necessity of integrating psychology into language teaching practices to optimize learning outcomes and nurture learners' linguistic and socio-emotional development. So far, no complete research has been done in this regard, and this article deals with this important issue for the first time. The research method is based on qualitative method and case studies, and the role of psychological principles in strengthening the learner's independence, increasing motivation, and facilitating language learning. Also, the optimization of learning results and fostering language and social development are among the findings of the research.

Keywords: language, teaching, psychology, methods

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Wei Hing Rosenkvist

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In the past few decades, concerns and demands of promoting student intercultural communicative competence in foreign language education have been increasing along with the rapid growth of information technologies and globalization in the 21st century. In Sweden, related concepts such as internationalization, global citizenship, multiculturalism, and intercultural communication etc., are also keywords that would be found in the written learning objectives of the foreign language education in all levels. Being one of the leading higher institutes in distance education in Europe, Dalarna University clearly states that after completion of the teacher education program, students shall understand the needs for integrating internationalization, intercultural and global perspective in teaching and learning in Swedish schools and implement their own studies to promote education in an international and global context. Despite the fact that many teachers and educators agree with the institutes’ mission and vision about the importance of internationalization and the need of increasing student understanding of intercultural and global perspective, they might find this objective unattainable and restricted due to the nature of the subject and their personal knowledge of intercultural competence. When conducting a comprehensive Chinese language course for the students who are going to become Chinese foreign language teachers, the researcher found that all the learning objectives are linguistic oriented while grammatical components dominate the entire course. Apparently, there is a gap between the learning objectives of the course and the DU’s mission of fostering an international learner with intercultural and globalized perspectives. How to include this macro-learning objective in a foreign language course is a great challenge to the educator. Although scholars from different academic domains have provided different theoretical frameworks and approaches for developing student intercultural competence, research that focuses on the didactic perspectives of developing student intercultural competence in teaching Chinese as a foreign language education (CFL) is limited and practical examples are rare. This has motivated the researcher to conduct an action research study that aims at integrating DU’s macro-learning objective in a current CFL course through different didactic practices with a purpose of developing the teacher student intercultural competence. This research study aims to, firstly, illustrate the cross-cultural knowledge integrated into the present Chinese language course for developing intercultural competence. Secondly, it investigates different didactic means that can be utilized to deliver cross-cultural knowledge to student teachers in the present course without generating dramatic disturbance of the syllabus. Thirdly, it examines the effectiveness of these didactic means in enhancing teacher student intercultural competence regarding the need for integrating and implementing internationalization, intercultural and global perspectives in teaching and learning in Swedish schools. Last but not least, it intends to serve as a practical example for developing the student teachers’ intercultural competence in foreign language education in DU and fill in the research gap of this academic domain worldwide.

Keywords: intercultural competence, foreign language education, action research, teacher education

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Authors: Brian Tung, Yam Wing Siu, Tsun Se Cheong

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Purpose: This research aims to explore how personal and environmental factors, especially the socio-economic changes and behavioral changes fostered by the COVID-19 outbreak pandemic, affect the financial vulnerability of a specific segment of people in financial distress. Innovative research methodology of machine learning will be applied to data collected from over 300 local individuals in Hong Kong seeking counseling or similar services in recent years. Results: First, machine learning has found that too much exposure to digital services and information on digitized services may lead to adverse effects on respondents’ financial vulnerability. Second, the improvement in financial literacy level provides benefits to the financially vulnerable group, especially those respondents who have started with a lower level. Third, serious addiction to digital technology can lead to worsened debt servicing ability. Machine learning also has found a strong correlation between debt servicing situations and income-seeking behavior as well as spending behavior. In addition, if the vulnerable groups are able to make appropriate investments, they can reduce the probability of incurring financial distress. Finally, being too active in borrowing and repayment can result in a higher likelihood of over-indebtedness. Conclusion: Findings can be employed in formulating a better counseling strategy for professionals. Debt counseling services can be more preventive in nature. For example, according to the findings, with a low level of financial literacy, the respondents are prone to overspending and unable to react properly to the e-marketing promotion messages pop-up from digital services or even falling into financial/investment scams. In addition, people with low levels of financial knowledge will benefit from financial education. Therefore, financial education programs could include tech-savvy matters as special features.

Keywords: personal finance, digitization of the economy, COVID-19 pandemic, addiction to digital technology, financial vulnerability

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Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

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Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

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Authors: Mangala. Sadasivan, Mary Hughes, Bryan Kelly

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Medical Education must focus on bridging the gap between teaching and learning when training pre-clinical year students in skills needed to keep up with medical knowledge and to meet the demands of health care in the future. The authors were interested in showing that a 3-S Model (building strength, developing stamina, and increasing speed) using a bridged curriculum design helps connect teaching and learning and improves students’ retention of basic science and clinical knowledge. The authors designed three learning modules using the 3-S Model within a systems course in a pre-clerkship medical curriculum. Each module focused on a bridge (concept map) designed by the instructor for specific content delivered to students in the course. This with-in-subjects design study included 304 registered MSU osteopathic medical students (3 campuses) ranked by quintile based on previous coursework. The instructors used the bridge to create self-directed learning exercises (building strength) to help students master basic science content. Students were video coached on how to complete assignments, and given pre-tests and post-tests designed to give them control to assess and identify gaps in learning and strengthen connections. The instructor who designed the modules also used video lectures to help students master clinical concepts and link them (building stamina) to previously learned material connected to the bridge. Boardstyle practice questions relevant to the modules were used to help students improve access (increasing speed) to stored content. Unit Examinations covering the content within modules and materials covered by other instructors teaching within the units served as outcome measures in this study. This data was then compared to each student’s performance on a final comprehensive exam and their COMLEX medical board examinations taken some time after the course. The authors used mean comparisons to evaluate students’ performances on module items (using 3-S Model) to non-module items on unit exams, final course exam and COMLEX medical board examination. The data shows that on average, students performed significantly better on module items compared to non-module items on exams 1 and 2. The module 3 exam was canceled due to a university shut down. The difference in mean scores (module verses non-module) items disappeared on the final comprehensive exam which was rescheduled once the university resumed session. Based on Quintile designation, the mean scores were higher for module items than non-module items and the difference in scores between items for Quintiles 1 and 2 were significantly better on exam 1 and the gap widened for all Quintile groups on exam 2 and disappeared in exam 3. Based on COMLEX performance, all students on average as a group, whether they Passed or Failed, performed better on Module items than non-module items in all three exams. The gap between scores of module items for students who passed COMLEX to those who failed was greater on Exam 1 (14.3) than on Exam 2 (7.5) and Exam 3 (10.2). Data shows the 3-S Model using a bridge effectively connects teaching and learning

Keywords: bridging gap, medical education, teaching and learning, model of learning

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Authors: K. P. Fasalu Rahman

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The financial services industry of Islam include large number of institutions, such as investment banks and commercial banks, investment companies and mutual insurance companies. All types of these financial institutions should have to deal with many issues and risks in their field of work. Islamic banks should expect to face two types of risks: risks that are similar to those faced by conventional financial intermediaries and risks that are unique to the Islamic Banks due to their compliance with the Shariah. The use of financial services and products that comply with the Shariah principles cause special issues for supervision and risk management. Risks are uncertain future events that could influence the achievement of the bank’s objectives, including strategic, operational, financial and compliance objectives. In Islamic banks, effective risk management deserves special attention. As an operational problem, risk management is the classification and identification of methods, processes, and risks in banks to supervise, monitor and measure them. In comparison to conventional banks, Islamic banks face big difficulties in identifying and managing risks due to bigger complexities emerging from the profit loss sharing (PLS) concept and nature of particular risks of Islamic financing. As the developing of managing risks tool becomes very essential, especially in Islamic banking as most of the products are depending on PLS principle, identifying and measuring each type of risk is highly important and critical in any Islamic finance based systems. This paper highlights the special and general risks surrounding Islamic banking. And it investigates in detail the need for risk management in Islamic banks. In addition to analyzing the effectiveness of risk management strategies adopted by Islamic financial institutions at present, this research is also suggesting strategies for improving risk management process of Islamic banks in future.

Keywords: Islamic banking, management, risk, risk management

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Authors: Kamlesh Shashikant Dave

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The global financial crisis is rooted in the sub-prime crisis in U.S.A. During the boom years, mortgage brokers attracted by the big commission, encouraged buyers with poor credit to accept housing mortgages with little or no down payment and without credit check. A combination of low interest rates and large inflow of foreign funds during the booming years helped the banks to create easy credit conditions for many years. Banks lent money on the assumptions that housing price would continue to rise. Also the real estate bubble encouraged the demand for houses as financial assets .Banks and financial institutions later repackaged these debts with other high risk debts and sold them to worldwide investors creating financial instruments called collateral debt obligations (CDOs). With the rise in interest rate, mortgage payments rose and defaults among the subprime category of borrowers increased accordingly. Through the securitization of mortgage payments, a recession developed in the housing sector and consequently it was transmitted to the entire US economy and rest of the world. The financial credit crisis has moved the US and the global economy into recession. Indian economy has also affected by the spill over effects of the global financial crisis. Great saving habit among people, strong fundamentals, strong conservative and regulatory regime have saved Indian economy from going out of gear, though significant parts of the economy have slowed down. Industrial activity, particularly in the manufacturing and infrastructure sectors decelerated. The service sector too, slow in construction, transport, trade, communication, hotels and restaurants sub sectors. The financial crisis has some adverse impact on the IT sector. Exports had declined in absolute terms in October. Higher inputs costs and dampened demand have dented corporate margins while the uncertainty surrounding the crisis has affected business confidence. To summarize, reckless subprime lending, loose monetary policy of US, expansion of financial derivatives beyond acceptable norms and greed of Wall Street has led to this exceptional global financial and economic crisis. Thus, the global credit crisis of 2008 highlights the need to redesign both the global and domestic financial regulatory systems not only to properly address systematic risk but also to support its proper functioning (i.e financial stability).Such design requires: 1) Well managed financial institutions with effective corporate governance and risk management system 2) Disclosure requirements sufficient to support market discipline. 3)Proper mechanisms for resolving problem institution and 4) Mechanisms to protect financial services consumers in the event of financial institutions failure.

Keywords: FIIs, BSE, sensex, global impact

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Authors: Eyibo Ebengeobong Eddie, Halil Zafer Alibaba

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The neglect of classroom acoustics is dominant in most educational facilities, meanwhile, hearing and listening is the learning process in this kind of facilities. A classroom should therefore be an environment that encourages listening, without an obstacles to understanding what is being taught. Although different studies have shown teachers to complain that noise is the everyday factor that causes stress in classroom, the capacity of individuals to understand speech is further affected by Echoes, Reverberation, and room modes. It is therefore necessary for classrooms to have an ideal acoustics to aid the intelligibility of students in the learning process. The influence of these acoustical parameters on learning and teaching in schools needs to be further researched upon to enhance the teaching and learning capacity of both teacher and student. For this reason, there is a strong need to provide and collect data to analyse and define the suitable quality of classrooms needed for a learning environment. Research has shown that acoustical problems are still experienced in both newer and older schools. However, recently, principle of acoustics has been analysed and room acoustics can now be measured with various technologies and sound systems to improve and solve the problem of acoustics in classrooms. These acoustic solutions, materials, construction methods and integration processes would be discussed in this paper.

Keywords: classroom, acoustics, materials, integration, speech intelligibility

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Authors: Kai Qian, Lixin Tao

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Mobile devices such as smartphones are getting more and more popular in our daily lives. The security vulnerability and threat attacks become a very emerging and important research and education topic in computing security discipline. There is a need to have an innovative mobile security hands-on laboratory to provide students with real world relevant mobile threat analysis and protection experience. This paper presents an authentic teaching and learning mobile security approach with smartphone devices which covers most important mobile threats in most aspects of mobile security. Each lab focuses on one type of mobile threats, such as mobile messaging threat, and conveys the threat analysis and protection in multiple ways, including lectures and tutorials, multimedia or app-based demonstration for threats analysis, and mobile app development for threat protections. This authentic learning approach is affordable and easily-adoptable which immerse students in a real world relevant learning environment with real devices. This approach can also be applied to many other mobile related courses such as mobile Java programming, database, network, and any security relevant courses so that can learn concepts and principles better with the hands-on authentic learning experience.

Keywords: mobile computing, Android, network, security, labware

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Authors: Martina Holenko Dlab, Natasa Hoic-Bozic

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This paper presents an original approach to student and group activity level assessment that relies on certainty factors theory. Activity level is used to represent quantity and continuity of student’s contributions in individual and collaborative e‑learning activities (e‑tivities) and is calculated to assist teachers in assessing quantitative aspects of student's achievements. Calculated activity levels are also used to raise awareness and provide recommendations during the learning process. The proposed approach was implemented within the educational recommender system ELARS and validated using data obtained from e‑tivity realized during a blended learning course. The results showed that the proposed approach can be used to estimate activity level in the context of e-tivities realized using Web 2.0 tools as well as to facilitate the assessment of quantitative aspect of students’ participation in e‑tivities.

Keywords: assessment, ELARS, e-learning, recommender systems, student model

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Authors: Priyadarshini Kiran, Gulshan Kumar

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: Social media are becoming the most important tools for interaction among learners, pedagogues and parents where everybody can share, exchange, comment, discuss and create information and knowledge in a collaborative way. The present case study attempts to highlight the role of social media (WhatsApp) in nurturing learning habits in toddlers with the help of parents in primary education. The Case study is based on primary data collected from a primary school situated in a small town in the northern state of Uttar Pradesh, India. In research methodology, survey and structured interviews have been used as a tool collected from parents and pedagogues. The findings Suggest: - To nurture learning habits in toddlers, parents and pedagogues use social media site (WhatsApp) in real-time and that too is convenient and handy; - Skill enhancement on the part of Pedagogues as a result of employing innovative teaching-learning techniques; - Social media sites serve as a social connectivity tool to ward off negativity and monotony on the part of parents and pedagogues in the wake of COVID- 19

Keywords: innovative teaching-learning techniques, pedagogues, social media, nurture, toddlers

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Authors: Onesimus Bulus Shiaki

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The study investigated the effect of cooperative learning strategy on mathematics achievement and retention among senior secondary school students of different abilities in Taraba State Nigeria. Cooperative learning strategy could hopefully contribute to students’ achievement which will spur the teachers to develop strategies for better learning. The quasi-experimental of pretest, posttest and control group design was adopted in this study. A sample of one hundred and sixty-four (164) Senior Secondary Two (SS2) students were selected from a population of twelve thousand, eight hundred and seventy-three (12,873) SS2 Students in Taraba State. Two schools with equivalent mean scores in the pre-test were randomly assigned to experimental and control groups. The experimental group students were stratified according to ability levels of low, medium and high. The experimental group was guided by the research assistants using the cooperative learning instructional package. After six weeks post-test was administered to the two groups while the retention test was administered two weeks after the post-test. The researcher developed a 50-item Mathematics Achievement Test (MAT) which was validated by experts obtaining the reliability coefficient of 0.87. Mean scores and standard deviations were used to answer the research questions while the Analysis of Co-variance (ANCOVA) was used to test the hypotheses. Major findings from the statistical analysis showed that cooperative learning strategy has a significant effect on the mean achievement of students as well as retention among students of high, medium and low ability in mathematics. However, cooperative learning strategy has no effect on the interaction of ability level and retention. Based on the results obtained, it was therefore recommended that the adoption of the use of cooperative learning strategy in the teaching and learning of mathematics in senior secondary schools be initiated, maintained and sustained for the benefit of senior secondary school students in Taraba State. Periodic Government sponsored in-service training in form of long vacation training programme, workshops, conferences and seminars on the nature, scope, and use of cooperative learning strategy should be organized for senior secondary school mathematics teachers in Taraba state.

Keywords: ability level, cooperative learning, mathematics achievement, retention

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Authors: Maria Isabel Garcia-Planas, Maria Victoria Garcia-Camba

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The use of brain stem auditory evoked potential (BAEP) is a common way to study the auditory function of people, a way to learn the functionality of a part of the brain neuronal groups that intervene in the learning process by studying the behaviour of wave VI. The latest advances in neuroscience have revealed the existence of different brain activity in the learning process that can be highlighted through the use of innocuous, low-cost, and easy-access techniques such as, among others, the BAEP that can help us to detect early possible neurodevelopmental difficulties for their subsequent assessment and cure. To date and to the authors' best knowledge, only the latency data obtained, observing the first to V waves and mainly in the left ear, were taken into account. This work shows that it is essential to take into account both ears; with these latest data, it has been possible had diagnosed more precise some cases than with the previous data had been diagnosed as 'normal' despite showing signs of some alteration that motivated the new consultation to the specialist.

Keywords: ear, neurodevelopment, auditory evoked potentials, intervals of normality, learning disabilities

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Authors: Mohammad Alshehri, Fahd Alfarsi

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Efficient supply chains play an essential role in delivering humanitarian supplies and directly impact the success of public aid initiatives globally. Predicting the delivery status of these essential supplies in a timely manner is crucial. Therefore, this study explores the application of ensemble-learning approaches to predict whether humanitarian deliveries will be made on time, using a comprehensive case-study dataset provided by one of the largest international supplying organisation. We employed various machine learning methods, including AdaBoost, XGBoost, and Gradient Boosting, to develop our predictive model. Our findings demonstrate that ensemble algorithms achieved promising results, with F1 scores ranging from 0.90 to 0.98. These high accuracy levels indicate the robustness of ensemble-learning techniques in forecasting delivery status, potentially enabling more proactive and efficient supply chain management in global aid initiatives. The implications of this study suggest that integrating advanced predictive analytics can significantly enhance the reliability of supply chains, ensuring the timely delivery of critical commodities to those in need.

Keywords: humanitarian aids, supply chains, machine learning, delivery status

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Authors: Rashika Sharma

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The campuses at tertiary institutes can act as a social environment for peer to peer connections. However, socialization is not the only aspect that campuses provide. The campus can act as a learning environment that has often been termed as the campus curriculum. Many tertiary institutes have taken steps to make their campus a ‘green campus’ whereby initiatives have been taken to reduce their impact on the environment. However, as visible as these initiatives are, it is debatable whether these have any effect on students’ and their understanding of sustainable campus operations. Therefore, research was conducted to evaluate the effectiveness of sustainable campus operations in raising students’ awareness of sustainability. Students at two vocational institutes participated in this interpretive research with data collected through surveys and focus groups. The findings indicated that majority of vocational education students remained oblivious of sustainability initiatives on campuses.

Keywords: campus learning, education for sustainability, social learning, vocational education

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Authors: Hala Shaari, Nuredin Ahmed

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Teaching computer programming is recognized to be difficult and a real challenge. The biggest problem faced by novice programmers is their lack of understanding of basic programming concepts. A visualized learning tool was developed and used by volunteered first-year students for two semesters. The purposes of this paper are firstly, to emphasize factors which directly affect the performance of our students negatively. Secondly, to examine whether the proposed tool would improve their performance and learning progression. The results of adopting this tool were conducted using a pre-survey and post-survey questionnaire. As a result, students who used the learning tool showed better performance in their programming subject.

Keywords: factors, novice, programming, visualization

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Authors: Cahit Perkgöz

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Artificial intelligence has brought new approaches to solving problems in almost every research field in recent years. One of these topics is shape design and optimization, which has the possibility of applications in many fields, such as nanotechnology and electronics. A properly constructed cost function can eliminate the need for labeled data required in deep learning and create desired shapes. In this work, the network parameters are optimized differentially, which differs from traditional approaches. The methods are tested for physics-related structures and successful results are obtained. This work is supported by Eskişehir Technical University scientific research project (Project No: 20ADP090)

Keywords: deep learning, shape design, optimization, artificial intelligence

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Authors: Eniye Tebekaemi, Martin Zhao

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The dichotomy between the skills set of newly minted college graduates and the skills required by cybersecurity employers is on the rise. Colleges are struggling to cope with the rapid pace of technology evolution using outdated tools and practices. Industries are getting frustrated due to the need to retrain fresh college graduates on skills they should have acquired. There is a dire need for academic institutions to develop new tools and systems to deliver cybersecurity education to meet the ever-evolving technology demands of the industry. The Cyber-Softbook project’s goal is to bridge the tech industry and tech education gap by providing educators a framework to collaboratively design, manage, and deliver cybersecurity academic courses that meet the needs of the tech industry. The Cyber-Softbook framework, when developed, will provide a platform for academic institutions and tech industries to collaborate on tech education and for students to learn about cybersecurity with all the resources they need to understand concepts and gain valuable skills available on a single platform.

Keywords: cybersecurity, education, skills, labs, curriculum

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Authors: Shrabani Ghosh

Abstract:

Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques.

Keywords: attention learning, language model, offensive language detection, self-supervised learning

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Authors: Mohammad Rostami, Mohammad Reza Forghani, Elahe Neshat, Fatemeh Yaghoobi

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An Ad Hoc network consists of wireless mobile equipment which connects to each other without any infrastructure, using connection equipment. The best way to form a hierarchical structure is clustering. Various methods of clustering can form more stable clusters according to nodes' mobility. In this research we propose an algorithm, which allocates some weight to nodes based on factors, i.e. link stability and power reduction rate. According to the allocated weight in the previous phase, the cellular learning automaton picks out in the second phase nodes which are candidates for being cluster head. In the third phase, learning automaton selects cluster head nodes, member nodes and forms the cluster. Thus, this automaton does the learning from the setting and can form optimized clusters in terms of power consumption and link stability. To simulate the proposed algorithm we have used omnet++4.2.2. Simulation results indicate that newly formed clusters have a longer lifetime than previous algorithms and decrease strongly network overload by reducing update rate.

Keywords: mobile Ad Hoc networks, clustering, learning automaton, cellular automaton, battery power

Procedia PDF Downloads 397

Authors: Bernardita Tornero

Abstract:

Cognitive Acceleration in Mathematics Education (CAME) programmes have been used successfully for promoting the development of thinking skills in school students for the last 30 years. Given that the approach has had a tremendous impact on the thinking capabilities of participating students, this study explored the experience of using the programme with prospective primary teachers in Chile. Therefore, this study not only looked at the experience of prospective primary teachers during the CAME course as learners, but also examined how they perceived the approach from their perspective as future teachers, as well as how they could transfer the teaching strategies they observed to their future classrooms. Given the complexity of the phenomenon under study, this research used a mixed methods approach. For this reason, the impact that the CAME course had on prospective teachers’ thinking skills was not only approached by using a test that assessed the participants’ improvements in these skills, but their learning and teaching experiences were also recorded through qualitative research tools (learning journals, interviews and field notes). The main findings indicate that, at the end of the CAME course, prospective teachers not only demonstrated higher thinking levels, but also showed positive attitudinal changes towards teaching and learning in general, and towards mathematics in particular. The participants also had increased confidence in their ability to teach mathematics and to promote thinking skills in their students. In terms of the CAME methodology, prospective teachers not only found it novel and motivating, but also commented that dealing with the thinking skills topic during a university course was both unusual and very important for their professional development. This study also showed that, at the end of the CAME course, prospective teachers felt they had developed strategies that could be used in their classrooms in the future. In this context, the relevance of the study is not only that it described the impact and the positive results of the first experience of using a CAME approach with prospective teachers, but also that some of the conclusions have significant implications for the teaching of thinking skills and the training of primary school teachers.

Keywords: cognitive acceleration, formal reasoning, prospective teachers, initial teacher training

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Authors: Nubwimana Rachel, Mugabowindekwe Maurice

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Trees play a crucial role in maintaining biodiversity and providing various ecological services. Traditional methods of counting trees are time-consuming, and there is a need for more efficient techniques. However, deep learning makes it feasible to identify the multi-scale elements hidden in aerial imagery. This research focuses on the application of deep learning techniques for tree detection and counting in both forest and non-forest areas through the exploration of the deep learning application for automated tree detection and counting using satellite imagery. The objective is to identify the most effective model for automated tree counting. We used different deep learning models such as YOLOV7, SSD, and UNET, along with Generative Adversarial Networks to generate synthetic samples for training and other augmentation techniques, including Random Resized Crop, AutoAugment, and Linear Contrast Enhancement. These models were trained and fine-tuned using satellite imagery to identify and count trees. The performance of the models was assessed through multiple trials; after training and fine-tuning the models, UNET demonstrated the best performance with a validation loss of 0.1211, validation accuracy of 0.9509, and validation precision of 0.9799. This research showcases the success of deep learning in accurate tree counting through remote sensing, particularly with the UNET model. It represents a significant contribution to the field by offering an efficient and precise alternative to conventional tree-counting methods.

Keywords: remote sensing, deep learning, tree counting, image segmentation, object detection, visualization

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Authors: Kateriina Rannula, Elle Sõrmus, Siret Piirsalu

Abstract:

Mixed generation classroom is a phenomenon that current higher education establishments are faced with daily trying to meet the needs of modern labor market with its emphasis on lifelong learning and retraining. Representatives of mainly X and Y generations in one classroom acquiring higher education is a challenge to lecturers considering all the characteristics that differ one generation from another. The importance of outlining different strategies and considering the needs of the students lies in the necessity for everyone to acquire the maximum of the provided knowledge as well as to understand each other to study together in one classroom and successfully cooperate in future workplaces. In addition to different generations, there are also learners with different native languages which have an impact on reading and understanding texts in third languages, including possible translation. Current research aims to investigate, describe and compare reading strategies among the representatives of generation X and Y. Hypotheses were formulated - representatives of generation X and Y use different reading strategies which is also different among first and third year students of the before mentioned generations. Current study is an empirical, qualitative study. To achieve the aim of the research, relevant literature was analyzed and a semi-structured questionnaire conducted among the first and third year students of Tallinn Health Care College. Questionnaire consisted of 25 statements on the text reading strategies, 3 multiple choice questions on preferences considering the design and medium of the text, and three open questions on the translation process when working with a text in student’s third language. The results of the questionnaire were categorized, analyzed and compared. Both, generation X and Y described their reading strategies to be 'scanning' and 'surfing'. Compared to generation X, first year generation Y learners valued interactivity and nonlinear texts. Students frequently used strategies of skimming, scanning, translating and highlighting together with relevant-thinking and assistance-seeking. Meanwhile, the third-year generation Y students no longer frequently used translating, resourcing and highlighting while Generation X learners still incorporated these strategies. Knowing about different needs of the generations currently inside the classrooms and on the labor market enables us with tools to provide sustainable education and grants the society a work force that is more flexible and able to move between professions. Future research should be conducted in order to investigate the amount of learning and strategy- adoption between generations. As for reading, main suggestions arising from the research are as follows: make a variety of materials available to students; allow them to select what they want to read and try to make those materials visually attractive, relevant, and appropriately challenging for learners considering the differences of generations.

Keywords: generation X, generation Y, learning strategies, reading strategies

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Authors: Khaled Alotaibi

Abstract:

This paper deals with developing reasoning thinking and the intrinsic-extrinsic motivation for learning, and enhancing the academic achievement of a sample of students at Teachers' College in King Saud University. The study sample included 58 students who were divided randomly into two groups; one was an experimental group with 20 students and the other was a control group with 22 students. The following tools were used: e-courses by using I-map, Reasoning Thinking Tes, questionnaire to measure the intrinsic-extrinsic motivation for learning and an academic achievement test. Experimental group was taught using e-courses by using I-map, while the control group was taught by using traditional education. The results showed that: - There were no statistically significant differences between the experimental group and the control group in Reasoning thinking skills. - There were statistically significant differences between the experimental group and the control group in the intrinsic-extrinsic motivation for learning in favor of the experimental group. - There were statistically significant differences between the experimental group and the control group in academic achievement in favor of the experimental group.

Keywords: reasoning, thinking, intrinsic motivation, active learning

Procedia PDF Downloads 410