Search results for: learning evaluation

Authors: Iván García-Magariño, Javier Bravo-Agapito, Marta López-Fernández

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Online judges have proven their utility in partial auto-evaluation of programming short exercises in the last decades. UnitJudge online judge has the advantage of facilitating the evaluation of separate units to provide more segregate and meaningful feedback to students in complex exercises and practices. This paper discusses the use of UnitUdge in advanced Java object-oriented programming exercises and web development practices. This later usage has been proposed by means of the Selenium Java library and classes to provide the web address. Consequently, UnitJudge is an online judge system that can be applied in several subjects, and therefore, many other students would take advantage of self-testing their exercises. This paper presents the experiments with a Java programming exercise for learning Java object-oriented classes with a generic type. Considering 10 students who voluntarily used UnitJudge, 80% successfully learned this concept, passing the judge exercise with correct results.

Keywords: online judges, programming skills, computer science education, auto-evaluation

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Authors: Durri Andriani, Irsan Tahar, Lilian Sarah Hiariey

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Every institution has to develop, implement, and control its policies to ensure the effectiveness of the institution. In doing so, all related stakeholders have to be involved to maximize the benefit of the policies and minimize the potential constraints and resistances. Open and distance education (ODE) institution is no different. As an education institution, ODE institution has to focus their attention to fulfilling academic needs of their students through open and distance measures. One of them is quality learning support system. Significant stakeholders in learning support system are tutors since they are the ones who directly communicate with students. Tutors are commonly seen as objects whose main responsibility is limited to implementing policies decided by management in ODE institutions. Nonetheless, tutors’ perceptions of tutorials are believed to influence tutors’ performances in facilitating learning support. It is therefore important to analyze tutors’ perception on various aspects of learning support. This paper presents analysis of tutors’ perceptions on policies of tutoriala in ODE institution using Policy Analysis Framework (PAF) modified by King, Nugent, Russell, and Lacy. Focus of this paper is on on-line tutors, those who provide tutorials via Internet. On-line tutors were chosen to stress the increasingly important used of Internet in ODE system. The research was conducted in Universitas Terbuka (UT), Indonesia. UT is purposely selected because of its large number (1,234) of courses offered and large area coverage (6000 inhabited islands). These posed UT in a unique position where learning support system has, to some extent, to be standardized while at the same time it has to be able to cater the needs of different courses in different places for students with different backgrounds. All 598 listed on-line tutors were sent the research questionnaires. Around 20% of the email addresses could not be reached. Tutors were asked to fill out open-ended questionnaires on their perceptions on definition of on-line tutorial, roles of tutors and students in on-line tutorials, requirement for on-line tutors, learning materials, and student evaluation in on-line tutorial. Data analyzed was gathered from 40 on-line tutors who sent back filled-out questionnaires. Data were analyzed qualitatively using content analysis from all 40 tutors. The results showed that using PAF as entry point in choosing learning support services as area of policy with delivery learning materials as the issue at UT has been able to provide new insights of aspects need to be consider in formulating policies in online tutorial and in learning support services. Involving tutors as source of information could be proven to be productive. In general, tutors had clear understanding about definition of online tutorial, roles of tutors and roles of students, and requirement of tutor. Tutors just need to be more involved in the policy formulation since they could provide data on students and problem faced in online tutorial. However, tutors need an adjustment in student evaluation which according tutors too focus on administrative aspects and subjective.

Keywords: distance education, on-line tutorial, tutorial policy, tutors’ perspectives

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Authors: Rasha Alshaye

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Blended learning has become an innovative model that combines face-to-face with e-learning approaches. The Saudi Electronic University (SEU) has adopted blended learning as a flexible approach that provides instructors and learners with a motivating learning environment to stimulate the teaching and learning process. This study investigates the perceptions of English language instructors, teaching the four English language skills at Saudi Electronic University. Four main domains were examined in this study; challenges that the instructors encounter while implementing the blended learning approach, enhancing student-instructor interaction, flexibility in teaching, and the lack of technical skills. Furthermore, the study identifies and represents the instructors’ aspirations and plans to utilize this approach in enhancing the teaching and learning experience. Main findings indicate that instructors at Saudi Electronic University experience some challenges while teaching the four language skills. However, they find the blended learning approach motivating and flexible for them and their students. This study offers some important understandings into how instructors are applying the blended learning approach and how this process can be enriched.

Keywords: blended learning, English language skills, English teaching, instructors' perceptions

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Authors: Long Chen

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With the proliferation of E-Learning, collaborative learning becomes more and more popular in various teaching and learning occasions. Studies over the years have proved that actively participating in classroom discussion can enhance student's learning experience, consolidating their knowledge and understanding of the class content. Collaborative learning can also allow students to share their resources and knowledge by exchanging, absorbing, and observing one another's opinions and ideas. Community Question Answering (CQA) services are particularly suitable paradigms for collaborative learning, since it is essentially an online collaborative learning platform where one can get information from multiple sources for he/her to choose from. However, current CQA services have only achieved limited success in collaborative learning due to the uncertainty of answers' quality. In this paper, we predict the quality of answers in a CQA service, i.e. Yahoo! Answers, for the use of online education and distance learning, which would enable a student to find relevant answers and potential answerers more effectively and efficiently, and thus greatly increase students' user experience in CQA services. Our experiment reveals that the quality of answers is influenced by a series of factors such as asking time, relations between users, and his/her experience in the past. We also show that by modelling user's profile with our proposed personalized features, student's satisfaction towards the provided answers could be accurately estimated.

Keywords: Community Question Answering, Collaborative Learning, Log File, Co-Training

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Authors: Olusegun Omoluwa, Kolawole Israel Anthony

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Pupils with learning disabilities are found in every classroom, but because learning disabilities cannot be seen, the condition is often too neglected. Unless these pupils are recognised and treated, they are likely to become educational discards. This study consequently attempted to determine effects of self-monitoring strategy on homework completion among pupils with learning disabilities. Ninety (90) participants were engaged in the study. Pre-test, post-test, control group quasi experimental design was adopted. Purposive sampling technique was used to select pupils with evidence of learning disabilities from three primary schools in Ondo State. Findings showed that self-monitoring strategy was significant in enhancing homework completion among pupils with learning disabilities. However, gender and self-esteem did not significantly contribute to homework completion. The study therefore recommended that measures such that would uncover unsettling academic, psychological and emotional deficiencies of these pupils through appropriate diagnosis should be undertaken by the parents and teachers, in order for them to have a sense of belonging in the society.

Keywords: self monitoring, home work completion, learning dissabilities, learning

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Authors: Wan Jen Chang

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Back ground: Taiwan's current adult lifelong learning services have expanded from vocational training to universal lifelong learning. However, both the professional knowledge training of learning guidance and consulting services and the provision of adult online learning consulting service systems still need to be established. Purpose: The purposes of this study are as follows: 1. Analyze the professional training mechanism for cultivating adult lifelong learning consultation and coaching; 2. Explore the feasibility of constructing a system that uses network technology to provide adult learning consultation services. Research design: This study conducts a literature analysis of counseling and coaching policy reports on lifelong learning in European countries and the United States. There are two focus discussions were conducted with 15 lifelong learning scholars, experts and practitioners as research subjects. The following two topics were discussed and suggested: 1. The current situation, needs and professional ability training mechanism of "Adult Lifelong Learning Consulting and Services"; 2. Strategies for establishing an "Adult Lifelong Learning Consulting and Service internet System". Conclusion: 1.Based on adult lifelong learning consulting and service needs, plan a professional knowledge training and certification system.2.Adult lifelong learning consulting and service professional knowledge and skills training should include the use of network technology to provide consulting service skills.3.To establish an adult lifelong learning consultation and service system, the Ministry of Education should promulgate policies and measures at the central level and entrust local governments or private organizations to implement them.4.The adult lifelong learning consulting and service system can combine the national qualifications framework, private sector and NPO to expand learning consulting service partners.

Keywords: adult lifelong learning, profesional knowledge, consulting and service, network system

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Authors: Tingting Zou

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This paper firstly presents an overview of the situation of Xinjiang and Hotan, and describes the current status and features of Uyghur students’ English education. Then it summarizes the research on the theories of Third Language Acquisition and Foreign Language Learning Motivation at home and abroad. Further, through the data collected by the questionnaire, the paper points out the three main problems and causes of Uyghur students’ English learning in Hotan, Xinjiang. Finally, the paper draws a conclusion and puts forward some suggestions on how to improve their English learning quality based on the theory of Foreign Language Learning Motivation.

Keywords: countermeasures and difficulties, English learning, Hotan Xinjiang, Uyghur students

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Authors: Zhidong Zhang, Yingchen Yang

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In this study, a diagnostic assessment model for Mastery Engineering Learning was established based on a group of undergraduate students who studied in an engineering course. A diagnostic assessment model can examine both students' learning process and report achievement results. One very unique characteristic is that the diagnostic assessment model can recognize the errors and anything blocking students in their learning processes. The feedback is provided to help students to know how to solve the learning problems with alternative strategies and help the instructor to find alternative pedagogical strategies in the instructional designs. Dynamics is a core course in which is a common course being shared by several engineering programs. This course is a very challenging for engineering students to solve the problems. Thus knowledge acquisition and problem-solving skills are crucial for student success. Therefore, developing an effective and valid assessment model for student learning are of great importance. Diagnostic assessment is such a model which can provide effective feedback for both students and instructor in the mastery of engineering learning.

Keywords: diagnostic assessment, mastery learning, engineering, bayesian network model, learning processes

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: Duaa Al Maani, Andrew Roberts

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Students in higher education are expected to learn actively and independently. Whilst quite work has been done to understand the perceptions of students’ learning transition regarding independent learning, to author’s best knowledge, it seems relatively few published research on independent learning in studio-based subjects such as architecture. Another major issue in independent learning research concerned the inconsistency in terminology; there appears to be a paucity of research on its definition, challenges, and tools within the UK university sector. It is not always clear how independent learning works in practice, or what are the challenges that face students toward being independent learners. Accordingly, this paper seeks to highlight these problems by analyzing previous and current literature of independent learning, in addition, to measure students’ independence at the very begging of their first academic year and compare it with their level of learning independence at the end of the same year. Eighty-seven student enrolled in 2017/2018 at Cardiff University completed the Autonomous Learning Questionnaire in order to measure their level of learning independence. Students’ initial responses were very positive and showed high level of learning independence. Interestingly, these responses significantly decreased at the end of the year. Time management was the most obvious challenge facing students transition into higher education, and contrary to expectations, we found no effect of student maturity on their level of independence. Moreover, we found no significant differences among students’ gender, but we did find differences among nationalities.

Keywords: autonomous learning, first year, learning independence, transition

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Authors: Chaiwat Waree

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The purposes of the research were to develop online lessons to strengthen the learning process of Master's degree students majoring in Curriculum and Instruction at Suan Sunandha Rajabhat University; to achieve the efficiency criteria of 80/80; and to study the satisfaction of students who use online lessons to strengthen the learning process of Master’s degree students majoring in Curriculum and Instruction at Suan Sunandha Rajabhat University. The sample consisted of 40 University students studying in semester 1, academic year 2012. The sample was determined by Purposive Sampling. Selected students were from the class which the researcher was the homeroom tutor. The tutor was responsible for the teaching of learning process. Tools used in the study were online lessons, 60-point performance test, and evaluation test of satisfaction of students on online lessons. Data analysis yielded the following results; 83.66/88.29 efficiency of online lessons measured against the criteria; the comparison of performance before and after taking online lessons using t-test yielded 29.67. The statistical significance was at 0.05; the average satisfaction level of forty students on online lessons was 4.46 with standard deviation of 0.68.

Keywords: online, lessons, curriculum, instruction

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: Elana Mahboub, Lamar Bakhurji, Hind Alhindi, Sara Alesayi

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Duolingo is a revolutionary language learning platform that offers an interactive and accessible learning experience. Its gamified approach makes language learning engaging and enjoyable, with a diverse range of languages available. The platform's adaptive learning system tailors lessons to individual proficiency levels, ensuring a personalized and efficient learning journey. The incorporation of multimedia elements enhances the learning experience and promotes practical language application. Duolingo's success is attributed to its mobile accessibility, offering basic access to language courses for free, with optional premium features for those seeking additional resources. Research shows positive outcomes for users, and the app's global impact extends beyond individual learning to formal language education initiatives. Duolingo is a transformative force in language education, breaking down barriers and making language learning an attainable goal for millions worldwide.

Keywords: duolingo, artificial intelligence, artificial intelligence in duolingo, benefit of artificial intelligence

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Authors: Ian Omung'a

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Breast cancer remains to be one of the deadliest cancers for women worldwide, with the risk of developing tumors being as high as 50 percent in Sub-Saharan African countries like Kenya. With as many as 42 percent of these cases set to be diagnosed late when cancer has metastasized and or the prognosis has become terminal, Full Field Digital [FFD] Mammography remains an effective screening technique that leads to early detection where in most cases, successful interventions can be made to control or eliminate the tumors altogether. FFD Mammograms have been proven to multiply more effective when used together with Computer-Aided Detection and Diagnosis [CADe] systems, relying on algorithmic implementations of Deep Learning techniques in Computer Vision to carry out deep pattern recognition that is comparable to the level of a human radiologist and decipher whether specific areas of interest in the mammogram scan image portray abnormalities if any and whether these abnormalities are indicative of a benign or malignant tumor. Within this paper, we review emergent Deep Learning techniques that will prove relevant to the development of State-of-The-Art FFD Mammogram CADe systems. These techniques will span self-supervised learning for context-encoded occlusion, self-supervised learning for pre-processing and labeling automation, as well as the creation of a standardized large-scale mammography dataset as a benchmark for CADe systems' evaluation. Finally, comparisons are drawn between existing practices that pre-date these techniques and how the development of CADe systems that incorporate them will be different.

Keywords: breast cancer diagnosis, computer aided detection and diagnosis, deep learning, whole mammogram classfication, ultrasound classification, computer vision

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Authors: Wang Lin, Li Zhiqiang

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The purpose of this paper is to analyze the cooperative learning behavior pattern based on the data of students' movement. The study firstly reviewed the cooperative learning theory and its research status, and briefly introduced the k-means clustering algorithm. Then, it used clustering algorithm and mathematical statistics theory to analyze the activity rhythm of individual student and groups in different functional areas, according to the movement data provided by 10 first-year graduate students. It also focused on the analysis of students' behavior in the learning area and explored the law of cooperative learning behavior. The research result showed that the cooperative learning behavior analysis method based on movement data proposed in this paper is feasible. From the results of data analysis, the characteristics of behavior of students and their cooperative learning behavior patterns could be found.

Keywords: behavior pattern, cooperative learning, data analyze, k-means clustering algorithm

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Authors: John Pomerat, Aviv Segev

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Recently, deep learning has had many theoretical breakthroughs. For deep learning to be successful in the industry, however, there need to be practical algorithms capable of handling many real-world hiccups preventing the immediate application of a learning algorithm. Although AI promises to revolutionize the healthcare industry, getting access to patient data in order to train learning algorithms has not been easy. One proposed solution to this is data- sharing. In this paper, we propose an alternative protocol, based on multi-party computation, to train deep learning models while maintaining both the privacy and security of training data. We examine three methods of training neural networks in this way: Transfer learning, average ensemble learning, and series network learning. We compare these methods to the equivalent model obtained through data-sharing across two different experiments. Additionally, we address the security concerns of this protocol. While the motivating example is healthcare, our findings regarding multi-party computation of neural network training are purely theoretical and have use-cases outside the domain of healthcare.

Keywords: neural network aggregation, multi-party computation, transfer learning, average ensemble learning

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Authors: Aviv Yaniv, Ron Ben Arosh, Nadav Gasner, Michael Konviser, Arbel Yaniv

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This paper introduces two methods for the evaluation of Question Decomposition Meaning Representation (QDMR) as predicted by sequence-to-sequence model and COPYNET parser for natural language questions processing, motivated by the fact that previous evaluation metrics used for this task do not take into account some characteristics of the representation, such as partial ordering structure. To this end, several heuristics to extract such partial dependencies are formulated, followed by the hereby proposed evaluation methods denoted as Proportional Graph Matcher (PGM) and Conversion to Normal String Representation (Nor-Str), designed to better capture the accuracy level of QDMR predictions. Experiments are conducted to demonstrate the efficacy of the proposed evaluation methods and show the added value suggested by one of them- the Nor-Str, for better distinguishing between high and low-quality QDMR when predicted by models such as COPYNET. This work represents an important step forward in the development of better evaluation methods for QDMR predictions, which will be critical for improving the accuracy and reliability of natural language question-answering systems.

Keywords: NLP, question answering, question decomposition meaning representation, QDMR evaluation metrics

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Authors: Kamal Heidari

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Covid-19 has borne drastic effects on different areas of society, including the education area, in that it brought virtual education to the center of attention, as an alternative to in-person education. In virtual education, the importance of flipped learning doubles, as students are supposed to take the main responsibility of teaching/learning process; and teachers play merely a facilitative/monitoring role. Given the students’ responsibility in virtual flipped learning, students’ motivation plays a pivotal role in the effectiveness of this learning method. The L2 Motivational Self-System (L2MSS) model is a currently proposed model elaborating on students’ motivation based on three sub-components: ideal L2 self, ought-to L2 self, and L2 learning experience. Drawing on an exploratory sequential mixed-methods research design, this study probed the effect of virtual flipped learning (via SHAD platform) on 112 gifted and non-gifted students’ motivation based on the L2 MSS. This study uncovered that notwithstanding the point that virtual flipped learning improved both gifted and non-gifted students’ motivation, it differentially affected their motivation. In other words, gifted students mostly referred to ideal L2 self, while non-gifted ones referred to ought-to L2 self and L2 learning experience aspects of motivation.

Keywords: virtual flipped learning, giftedness, motivation, L2MSS

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Authors: Evgeny Pyshkin, Maxim Mozgovoy, Vladislav Volkov

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The paper follows a discourse on computer-assisted language learning. We examine problems of foreign language teaching and learning and introduce a metamodel that can be used to define learning models of language grammar structures in order to support teacher/student interaction. Special attention is paid to the concept of a virtual language lab. Our approach to language education assumes to encourage learners to experiment with a language and to learn by discovering patterns of grammatically correct structures created and managed by a language expert.

Keywords: computer-assisted instruction, language learning, natural language grammar models, HCI

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Authors: Vijayan Narayananayar

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Identifying learning gaps is crucial in ensuring learners have the necessary knowledge and skills to succeed. The Learning and Teaching (L&T) approach requires tutors to identify gaps in knowledge and improvise learning activities to close them. One approach to identifying learning gaps is through diagnostic assessment, which uses well-structured questions and answer options. The paper focuses on the use of diagnostic assessment as a learning and teaching approach in a foundational module at a polytechnic. The study used diagnostic assessment over two semesters, including the COVID and post-COVID semesters, to identify gaps in learning. The design of the diagnostic activity, pedagogical intervention, and survey responses completed by learners were analyzed. Results showed that diagnostic assessment can be an effective tool for identifying learning gaps and designing interventions to address them. Additionally, the use of diagnostic assessment provides an opportunity for tutors to engage with learners on a one-to-one basis, tailoring teaching to individual needs. The paper also discusses the design of diagnostic questions and answer options, including characteristics that need to be considered in achieving the target of identifying learning gaps. The implications of using diagnostic assessment as a learning and teaching approach include bridging the gap between theory and practice, and ensuring learners are equipped with skills necessary for their future careers. This paper can be useful in helping educators and practitioners to incorporate diagnostic assessment into their L&T approach.

Keywords: assessment, learning & teaching, diagnostic assessment, analytics

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Authors: Annette McArthur

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The traditional model of teaching and learning, where ICT sits as a separate entity is not a model for a 21st century school. It is imperative that teaching and learning embraces technological advancements. The challenge in schools lies in shifting the mindset of teachers so they see ICT as integral to their teaching, learning and curriculum rather than a separate E-Learning curriculum stream. This research project investigates how the effective, planned, intentional integration of ICT into a STEM curriculum, can enable the shift in the teacher mindset. The project incorporated: • Developing a professional coaching relationship with key STEM teachers. • Facilitating staff professional development involving student centered project based learning pedagogy in the context of a STEM curriculum. • Facilitating staff professional development involving digital literacy. • Establishing a professional community where collaboration; sharing and reflection were part of the culture of the STEM community. • Facilitating classroom support for the effective delivery innovative STEM curriculum. • Developing STEM learning spaces where technologies were used to empower and engage learners to participate in student-centered, project-based learning.

Keywords: e-learning, ICT, project based learning, STEM

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Authors: Chechen Liao, Chin Yi Yang

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Although several factors that affect learning to program have been identified over the years, there continues to be no indication of any consensus in understanding why some students learn to program easily and quickly while others have difficulty. Seldom have researchers considered the problem of how to help the students enhance the programming learning outcome. The research had been conducted at a high school in Taiwan. Students participating in the study consist of 330 tenth grade students enrolled in the Basic Computer Concepts course with the same instructor. Two types of training methods-instruction-oriented and exploration-oriented were conducted. The result of this research shows that the instruction-oriented training method has better learning performance than exploration-oriented training method.

Keywords: learning performance, programming learning, TDD, training method

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Nurul Yusnita

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Many studies have been developed to help the students to get good achievement in English learning. They can be from the teaching method or psychological ones. One of the psychological studies in educational research is learning style. In some ways, learning style can affect the achievement of the students. This study aimed to examine 4 (four) learning styles and their relations to English achievement among the students learning English in Language Training Center of Universitas Muhammadiyah Yogyakarta (LTC UMY). The method of this study was descriptive analytical. The sample consisted of 39 Accounting students in LTC UMY. The data was collected through questionnaires with Likert-scale. The achievement was obtained from the grade of the students. To analyze the questionnaires and to see the relation between the learning styles and the student achievement, SPSS statistical software of correlational analysis was used. The result showed that both visual and auditory had the same percentage of 35.9% (14 students). 3 students (7.7%) had kinaesthetic learning style and 8 students (20.5%) had visual and auditory ones. Meanwhile, there were 5 students (12.8%) who had visual learning style could increase their grades. Only 1 student (2.5%) who had visual and auditory could improve his grade. Besides grade increase, there were also grade decrease. Students with visual, auditory, visual and auditory, and kinaesthetic learning styles were 3 students (7.7%), 5 students (12%), 4 students (10.2%) and 1 student (2.5%) respectively. In conclusion, there was no significant relationship between learning style and English achievement. Most of the good achievers were the students with visual and auditory learning styles and most of them preferred visual method. The implication is the teachers and material designers could improve their method through visual things to achieve effective English teaching learning.

Keywords: accounting students, English achievement, language training centre, learning styles

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Authors: Yen-Mei Lee

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Mobile learning (m-learning) has been applied in the education field not only because it is an alternative to web-based learning but also it possesses the ‘anytime, anywhere’ learning features. However, most studies focus on the technology-related issue, such as usability and functionality instead of addressing m-learning from the motivational perspective. Accordingly, the main purpose of the current paper is to integrate critical factors from different motivational theories and related findings to have a better understand the catalysts of an individual’s learning motivation toward m-learning. The main research question for this study is stated as follows: based on different motivational perspectives, what factors of applying mobile devices as medium can facilitate people’s learning motivations? Self-Determination Theory (SDT), Uses and Gratification Theory (UGT), Malone and Lepper’s taxonomy of intrinsic motivation theory, and different types of motivation concepts were discussed in the current paper. In line with the review of relevant studies, three motivating factors with five essential elements are proposed. The first key factor is autonomy. Learning on one’s own path and applying personalized format are two critical elements involved in the factor of autonomy. The second key factor is to apply a build-in instant feedback system during m-learning. The third factor is creating an interaction system, including communication and collaboration spaces. These three factors can enhance people’s learning motivations when applying mobile devices as medium toward learning. To sum up, in the currently proposed paper, with different motivational perspectives to discuss the m-learning is different from previous studies which are simply focused on the technical or functional design. Supported by different motivation theories, researchers can clearly understand how the mobile devices influence people’s leaning motivation. Moreover, instructional designers and educators can base on the proposed factors to build up their unique and efficient m-learning environments.

Keywords: autonomy, learning motivation, mobile learning (m-learning), motivational perspective

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Authors: Kornchanok Chidchaisuwan

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This project is a study of learning activities through experience to enhance career skills and technical abilities on the creative strategies for advertising course of undergraduate students. This instructional model consisted of study learning approaches: 1) Simulation-based learning: used to create virtual learning activities plans for work like working at advertising companies. 2) Project-based learning: Actual work based on the processed creating and focus on producing creative works to present on new media channels. The results of learning management found that there were effects on the students in various areas, including 1) The learners have experienced in the step by step of advertising work process. 2) The learner has the skills to work from the actual work (Learning by Doing), allowing the ability to create, present, and produce the campaign accomplished achievements and published on online media at a better level.

Keywords: technical, advertising, presentation, career skills, experience, simulation based learning

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Authors: Asmaa El Harat, Toumi Hicham, Youssef Baddi

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This survey delves into the complex realm of Internet of Things (IoT) security, highlighting the urgent need for effective cybersecurity measures as IoT devices become increasingly common. It explores a wide array of cyber threats targeting IoT devices and focuses on mitigating these attacks through the combined use of deep learning and machine learning algorithms, as well as edge and cloud computing paradigms. The survey starts with an overview of the IoT landscape and the various types of attacks that IoT devices face. It then reviews key machine learning and deep learning algorithms employed in IoT cybersecurity, providing a detailed comparison to assist in selecting the most suitable algorithms. Finally, the survey provides valuable insights for cybersecurity professionals and researchers aiming to enhance security in the intricate world of IoT.

Keywords: internet of things (IoT), cybersecurity, machine learning, deep learning

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Authors: Jatuphum Ketchatturat

Abstract:

The objective of this research was to study the learning and innovation skills of undergraduate students after Research-Based Learning had been applied. Eighty research participants were selected from undergraduate students enrolled in Educational Research Program using the Purposive Sampling Method. Research Methodology was Descriptive Research, the research took one semester to complete. The research instruments consisted of (1) Research Skill Assessment Form, (2) Research Quality Assessment Form, (3) Scale of learning and innovation skills 25 items. The quantitative data were analysed using descriptive statistics including, frequency, percentage, average and standard deviation. The qualitative data were analyzed using content analysis. The research results were (1) The students were able to conduct research that focused on educational research, which has a fair to the excellent level of standards of a research learning outcome, research skills, and research quality. The student’s learning and innovation skills have relating to research skills and research quality. (2) The findings found that the students have been developed to be learning and innovation skills such as systematic thinking, analytical thinking, critical thinking, creative problem solving, collaborative, research-creation, communication, and knowledge and experience sharing to friends, community and society.

Keywords: learning and innovation skills, research based learning, research skills, undergraduate students

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Authors: Sara ElElimy, Samir Moustafa

Abstract:

Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

Procedia PDF Downloads 126