Search results for: English language learning experiences

Authors: Mara Gabrielli, Jordi Pamies Rovira

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From a theoretical and interdisciplinary approach to visual ethnography and visual anthropology, this small scale, in-depth study explores the potential of photography as a participatory ethnographic method for a deep-understanding of the socio-educational integration of young refugees and asylum-seekers in the host society as regards their daily experiences, their needs, desires, expectations, and future goals. Qualitative data is collected by the author by observing 12 young participants in the age group 12-24 years per week for 12 months. The data consists of field notes, participatory observation, in-depth interviews with professionals, and the use of visual participatory ethnographic methods. Therefore, the young participants build their stories through the implementation of two participatory photographic methods - the 'photo-diary' and the 'photo-elicitation' - that permit them to analyse and narrate their social and educational experiences from their perspectives, thus collaborating in the construction of knowledge during the different stages of the research. Preliminary findings show the high resilience and social adaptability of young refugees and asylum-seekers to achieve their goals and overcome structural and socio-cultural barriers. However, the uncertainty of their administrative situation during the asylum submission and the lack of specific resources might impact negatively on their educational pathways and the transition to the labour market. Finally, this study also highlights the benefits of participatory photographic methods in ethnographic research, which impacts positively the well-being of these young people, helps them to develop critical thinking, and it also allows them to access information more respectfully when narrating painful experiences.

Keywords: photo-diary, photo-elicitation, resilience, strategies, visual methodologies, young refugees and asylum seekers

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Authors: Murad Almadani, Khalil Abu-Hantash, Xinyu Wang, Herbert Jelinek, Kinda Khalaf

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Gait analysis is used by healthcare professionals as a tool to gain a better understanding of the movement impairment and track progress. In most circumstances, monitoring patients in their real-life environments with low-cost equipment such as cameras and wearable sensors is more important. Inertial sensors, on the other hand, cannot provide enough information on angular dynamics. This research offers a method for tracking 2D joint coordinates using cutting-edge vision algorithms and a single RGB camera. We provide an end-to-end comprehensive deep learning pipeline for marker-less gait parameter estimation, which, to our knowledge, has never been done before. To make our pipeline function in real-time for real-world applications, we leverage the AlphaPose human posture prediction model and a deep learning transformer. We tested our approach on the well-known GPJATK dataset, which produces promising results.

Keywords: gait analysis, human pose estimation, deep learning, real time gait estimation, AlphaPose, transformer

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Authors: Klaus Peter Scherer, Daniel Knöll, Constantin Rieder

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Problem and Purpose: Intelligent systems are available and helpful to support the human being decision process, especially when complex surgical eye interventions are necessary and must be performed. Normally, such a decision support system consists of a knowledge-based module, which is responsible for the real assistance power, given by an explanation and logical reasoning processes. The interview based acquisition and generation of the complex knowledge itself is very crucial, because there are different correlations between the complex parameters. So, in this project (semi)automated self-learning methods are researched and developed for an enhancement of the quality of such a decision support system. Methods: For ophthalmic data sets of real patients in a hospital, advanced data mining procedures seem to be very helpful. Especially subgroup analysis methods are developed, extended and used to analyze and find out the correlations and conditional dependencies between the structured patient data. After finding causal dependencies, a ranking must be performed for the generation of rule-based representations. For this, anonymous patient data are transformed into a special machine language format. The imported data are used as input for algorithms of conditioned probability methods to calculate the parameter distributions concerning a special given goal parameter. Results: In the field of knowledge discovery advanced methods and applications could be performed to produce operation and patient related correlations. So, new knowledge was generated by finding causal relations between the operational equipment, the medical instances and patient specific history by a dependency ranking process. After transformation in association rules logically based representations were available for the clinical experts to evaluate the new knowledge. The structured data sets take account of about 80 parameters as special characteristic features per patient. For different extended patient groups (100, 300, 500), as well one target value as well multi-target values were set for the subgroup analysis. So the newly generated hypotheses could be interpreted regarding the dependency or independency of patient number. Conclusions: The aim and the advantage of such a semi-automatically self-learning process are the extensions of the knowledge base by finding new parameter correlations. The discovered knowledge is transformed into association rules and serves as rule-based representation of the knowledge in the knowledge base. Even more, than one goal parameter of interest can be considered by the semi-automated learning process. With ranking procedures, the most strong premises and also conjunctive associated conditions can be found to conclude the interested goal parameter. So the knowledge, hidden in structured tables or lists can be extracted as rule-based representation. This is a real assistance power for the communication with the clinical experts.

Keywords: an expert system, knowledge-based support, ophthalmic decision support, self-learning methods

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Authors: Edicio M. Faller

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The use of technology in the delivery of teaching and learning is vital nowadays especially in education. Computer Assisted Learning Module (CALM) software is the use of computer in the delivery of instruction with a tailored fit program intended for a specific lesson or a set of topics. The CALM software developed in this study is intended to supplement the traditional teaching methods in technical-vocational (TECH-VOC) instruction specifically the Consumer Electronics Servicing course. There are three specific objectives of this study. First is to create a learning enhancement and review materials on the selected lessons. Second, is to computerize the end-of-chapter quizzes. Third, is to generate a computerized mock exam and summative assessment. In order to obtain the objectives of the study the researcher adopted the Agile Model where the development of the study undergoes iterative and incremental process of the Software Development Life Cycle. The study conducted an acceptance testing using a survey questionnaire to evaluate the CALM software. The results showed that CALM software was generally interpreted as very satisfactory. To further improve the CALM software it is recommended that the program be updated, enhanced and lastly, be converted from stand-alone to a client/server architecture.

Keywords: computer assisted learning module, software development life cycle, computerized mock exam, consumer electronics servicing

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Authors: Shayan Mohajer Hamidi

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Federated learning has emerged as a promising approach for distributed model training while preserving data privacy. However, the challenges of communication overhead, limited network resources, and slow convergence hinder its widespread adoption. On the other hand, knowledge distillation has shown great potential in compressing large models into smaller ones without significant loss in performance. In this paper, we propose an innovative framework that combines federated learning and knowledge distillation to address these challenges and enhance the efficiency of distributed learning. Our approach, called Federated Knowledge Distillation (FKD), enables multiple clients in a federated learning setting to collaboratively distill knowledge from a teacher model. By leveraging the collaborative nature of federated learning, FKD aims to improve model compression while maintaining privacy. The proposed framework utilizes a coded teacher model that acts as a reference for distilling knowledge to the client models. To demonstrate the effectiveness of FKD, we conduct extensive experiments on various datasets and models. We compare FKD with baseline federated learning methods and standalone knowledge distillation techniques. The results show that FKD achieves superior model compression, faster convergence, and improved performance compared to traditional federated learning approaches. Furthermore, FKD effectively preserves privacy by ensuring that sensitive data remains on the client devices and only distilled knowledge is shared during the training process. In our experiments, we explore different knowledge transfer methods within the FKD framework, including Fine-Tuning (FT), FitNet, Correlation Congruence (CC), Similarity-Preserving (SP), and Relational Knowledge Distillation (RKD). We analyze the impact of these methods on model compression and convergence speed, shedding light on the trade-offs between size reduction and performance. Moreover, we address the challenges of communication efficiency and network resource utilization in federated learning by leveraging the knowledge distillation process. FKD reduces the amount of data transmitted across the network, minimizing communication overhead and improving resource utilization. This makes FKD particularly suitable for resource-constrained environments such as edge computing and IoT devices. The proposed FKD framework opens up new avenues for collaborative and privacy-preserving distributed learning. By combining the strengths of federated learning and knowledge distillation, it offers an efficient solution for model compression and convergence speed enhancement. Future research can explore further extensions and optimizations of FKD, as well as its applications in domains such as healthcare, finance, and smart cities, where privacy and distributed learning are of paramount importance.

Keywords: federated learning, knowledge distillation, knowledge transfer, deep learning

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Authors: Neeraj Malhotra

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Background: Bioreactors in tissue engineering are used as devices that apply mechanical means to influence biological processes. They are commonly employed for stem cell culturing, growth and expansion as well as in 3D tissue culture. Contemporarily there use is well established and is tested extensively in the medical sciences, for tissue-regeneration and tissue engineering of organs like bone, cartilage, blood vessels, skin grafts, cardiac muscle etc. Methodology: Literature search, both electronic and hand search, was done using the following MeSH and keywords: bioreactors, bioreactors and dentistry, bioreactors & dental tissue engineering, bioreactors and regenerative dentistry. Articles published only in English language were included for review. Results: Bioreactors like, spinner flask-, rotating wall-, flow perfusion-, and micro-bioreactors and in-vivo bioreactor have been employed and tested for the regeneration of dental and like-tissues. These include gingival tissue, periodontal ligament, alveolar bone, mucosa, cementum and blood vessels. Based on their working dynamics they can be customized in future for regeneration of pulp tissue and whole tooth regeneration. Apart from this, they have been successfully used in testing the clinical efficacy and biological safety of dental biomaterials. Conclusion: Bioreactors have potential use in testing dental biomaterials and tissue engineering approaches aimed at regenerative dentistry.

Keywords: bioreactors, biological process, mechanical stimulation, regenerative dentistry, stem cells

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Authors: Zohra Faize

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Globalization has expanded the mobility privileges of the Global North population while simultaneously, those in the Global South, namely poor, and racialized minorities are increasingly criminalized for crossing international borders. As part of this global trend, Canada also engages in tight border control practices, which often result in marginalization and criminalization of asylum seekers, including children. Using Interpretative Phenomenological Analysis as a theoretical framework and methodology, this research explores the effects of tight border control practices on children asylum-seekers; with a specific focus on detention experiences in Canadian prisons and immigration Holding Centers. The preliminary results of interviews with 8 participants confirm the violations of child rights that stem from the detention practice. Children also report that they find immigration detention to be a stressful and a confusing experience, often resulting in feeling of shame and guilt after their release into the community.

Keywords: border control, crimmigration, Canada, children asylum seekers, immcarceration, interpretative phenomenological analysis (IPA)

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Authors: Tzu-I Chiang

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A central question for finite-nonfinite distinction in Mandarin Chinese is how does Mandarin encode temporal information without the grammatical contrast between past and present tense. Moreover, how do L2 learners of Mandarin whose native language is English and whose L1 system has tense morphology, acquire the temporal encoding system in L2 Mandarin? The current study reports preliminary findings on the relationship between topic prominence and the temporal encoding in L1 and L2 Chinese. Oral narratives data from 30 natives and learners of Mandarin Chinese were collected via a film-retell task. In terms of coding, predicates collected from the narratives were transcribed and then coded based on four major verb types: n-degree Statives (quality-STA), point-scale Statives (status-STA), n-atom EVENT (ACT), and point EVENT (resultative-ACT). How native speakers and non-native speakers started retelling the story was calculated. Results of the study show that native speakers of Chinese tend to express Topic Time (TT) syntactically at the topic position; whereas L2 learners of Chinese across levels rely mainly on the default time encoded in the event types. Moreover, as the proficiency level of the learner increases, learners’ appropriate use of the event predicates increased, which supports the argument that L2 development of temporal encoding is affected by lexical aspect.

Keywords: topic prominence, temporal encoding, lexical aspect, L2 acquisition

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Authors: R. Verwiebe, L. Seewann, M. Wolf

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This presentation addresses marginalization and discrimination as experienced by young academics with a migrant background in the Austrian labor market. Focusing on second generation academics of Central Eastern European and Turkish descent we explore two major issues. First, we ask whether their career-entry and everyday professional life entails origin-specific barriers. As educational residents, they show competences which, when lacking, tend to be drawn upon to explain discrimination: excellent linguistic skills, accredited high-level training, and networks. Second, we concentrate on how this group reacts to discrimination and overcomes experiences of marginalization. To answer these questions, we utilize recent sociological and social psychological theories that focus on the diversity of individual experiences. This distinguishes us from a long tradition of research that has dealt with the motives that inform discrimination, but has less often considered the effects on those concerned. Similarly, applied coping strategies have less often been investigated, though they may provide unique insights into current problematic issues. Building upon present literature, we follow recent discrimination research incorporating the concepts of ‘multiple discrimination’, ‘subtle discrimination’, and ‘visual social markers’. 21 problem-centered interviews are the empirical foundation underlying this study. The interviewees completed their entire educational career in Austria, graduated in different universities and disciplines and are working in their first post-graduate jobs (career entry phase). In our analysis, we combined thematic charting with a coding method. The results emanating from our empirical material indicated a variety of discrimination experiences ranging from barely perceptible disadvantages to directly articulated and overt marginalization. The spectrum of experiences covered stereotypical suppositions at job interviews, the disavowal of competencies, symbolic or social exclusion by new colleges, restricted professional participation (e.g. customer contact) and non-recruitment due to religious or ethnical markers (e.g. headscarves). In these experiences the role of the academics education level, networks, or competences seemed to be minimal, as negative prejudice on the basis of visible ‘social markers’ operated ‘ex-ante’. The coping strategies identified in overcoming such barriers are: an increased emphasis on effort, avoidance of potentially marginalizing situations, direct resistance (mostly in the form of verbal opposition) and dismissal of negative experiences by ignoring or ironizing the situation. In some cases, the academics drew into their specific competences, such as an intellectual approach of studying specialist literature, focus on their intercultural competences or planning to migrate back to their parent’s country of origin. Our analysis further suggests a distinction between reactive (i.e. to act on and respond to experienced discrimination) and preventative strategies (applied to obviate discrimination) of coping. In light of our results, we would like to stress that the tension between educational and professional success experienced by academics with a migrant background – and the barriers and marginalization they continue to face – are essential issues to be introduced to socio-political discourse. It seems imperative to publicly accentuate the growing social, political and economic significance of this group, their educational aspirations, as well as their experiences of achievement and difficulties.

Keywords: coping strategies, discrimination, labor market, second generation university graduates

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Authors: Siti Navila

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Hedging as a kind of pragmatic competence is an essential part in achieving the goal in communication, especially in academic discourse where the process of sharing knowledge among academic community takes place. Academic discourse demands an appropriateness and modesty of an author or speaker in stating arguments, to name but few, by considering the politeness, being cautious and tentative, and differentiating personal opinions and facts in which these aspects can be achieved through hedging. This study was conducted to find the hedging devices used by students as well as to analyze how they use them in their oral presentation. Some oral presentations from English Department students of the State University of Jakarta on their Academic Presentation course final test were recorded and explored formally and functionally. It was found that the most frequent hedging devices used by students were shields from all hedging devices that students commonly used when they showed suggestion, stated claims, showed opinion to provide possible but still valid answer, and offered the appropriate solution. The researcher suggests that hedging can be familiarized in learning, since potential conflicts that is likely to occur while delivering ideas in academic contexts such as disagreement, criticism, and personal judgment can be reduced with the use of hedging. It will also benefit students in achieving the academic competence with an ability to demonstrate their ideas appropriately and more acceptable in academic discourse.

Keywords: academic discourse, hedging, hedging devices, lexical hedges, Meyer classification

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Authors: Mihail Calalb

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The aim of this research is to identify ways for achieving sustainable conceptual understanding within science lessons. For this purpose, a set of teaching and learning strategies, parts of the theory of visible teaching and learning (VTL), is studied. As a result, a new didactic approach named "learning by being" is proposed and its correlation with educational paradigms existing nowadays in science teaching domain is analysed. In the context of VTL the author describes the main strategies of "learning by being" such as guided self-scaffolding, structuring of information, and recurrent use of previous knowledge or help seeking. Due to the synergy effect of these learning strategies applied simultaneously in class, the impact factor of learning by being on cognitive achievement of students is up to 93 % (the benchmark level is equal to 40% when an experienced teacher applies permanently the same conventional strategy during two academic years). The key idea in "learning by being" is the assumption by the student of cognitive goals. From this perspective, the article discusses the role of student’s personal learning effort within several teaching strategies employed in VTL. The research results emphasize that three mandatory student – related moments are present in each constructivist teaching approach: a) students’ personal learning effort, b) student – teacher mutual feedback and c) metacognition. Thus, a successful educational strategy will target to achieve an involvement degree of students into the class process as high as possible in order to make them not only know the learning objectives but also to assume them. In this way, we come to the ownership of cognitive goals or students’ deep intrinsic motivation. A series of approaches are inherent to the students’ ownership of cognitive goals: independent research (with an impact factor on cognitive achievement equal to 83% according to the results of VTL); knowledge of success criteria (impact factor – 113%); ability to reveal similarities and patterns (impact factor – 132%). Although it is generally accepted that the school is a public service, nonetheless it does not belong to entertainment industry and in most of cases the education declared as student – centered actually hides the central role of the teacher. Even if there is a proliferation of constructivist concepts, mainly at the level of science education research, we have to underline that conventional or frontal teaching, would never disappear. Research results show that no modern method can replace an experienced teacher with strong pedagogical content knowledge. Such a teacher will inspire and motivate his/her students to love and learn physics. The teacher is precisely the condensation point for an efficient didactic strategy – be it constructivist or conventional. In this way, we could speak about "hybridized teaching" where both the student and the teacher have their share of responsibility. In conclusion, the core of "learning by being" approach is guided learning effort that corresponds to the notion of teacher–student harmonic oscillator, when both things – guidance from teacher and student’s effort – are equally important.

Keywords: conceptual understanding, learning by being, ownership of cognitive goals, science learning

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Authors: Amal Khalifa, Nicolas Vana Santos

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Steganography has been known for centuries as an efficient approach for covert communication. Due to its popularity and ease of access, image steganography has attracted researchers to find secure techniques for hiding information within an innocent looking cover image. In this research, we propose a novel deep-learning approach to digital image steganography. The proposed method, DeepWaveletFusion, uses convolutional neural networks (CNN) to hide a secret image into a cover image of the same size. Two CNNs are trained back-to-back to merge the Discrete Wavelet Transform (DWT) of both colored images and eventually be able to blindly extract the hidden image. Based on two different image similarity metrics, a weighted gain function is used to guide the learning process and maximize the quality of the retrieved secret image and yet maintaining acceptable imperceptibility. Experimental results verified the high recoverability of DeepWaveletFusion which outperformed similar deep-learning-based methods.

Keywords: deep learning, steganography, image, discrete wavelet transform, fusion

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Authors: Ahlem Abid, Farah Jemili

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Intrusion detection has been the subject of numerous studies in industry and academia, but cyber security analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve intrusion detection system, the visualisation of the security events in form of graphs and diagrams is important to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and using a machine learning graph algorithm which can detect in real time different attacks as early as possible. We use the MAWILab intrusion detection dataset . We choose Microsoft Azure as a unified cloud environment to load our dataset on. We implement the k2 algorithm which is a graphical machine learning algorithm to classify attacks. Our system showed a good performance due to the graphical machine learning algorithm and spark structured streaming engine.

Keywords: Apache Spark Streaming, Graph, Intrusion detection, k2 algorithm, Machine Learning, MAWILab, Microsoft Azure Cloud

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Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

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Authors: K. O'Malley

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Background: The emergence and rapid evolution of large language models (LLMs) (i.e., models of generative artificial intelligence, or AI) has been unprecedented. ChatGPT is one of the most widely used LLM platforms. Using natural language processing technology, it generates customized responses to user prompts, enabling it to mimic human conversation. Responses are generated using predictive modeling of vast internet text and data swathes and are further refined and reinforced through user feedback. The popularity of LLMs is increasing, with a growing number of students utilizing these platforms for study and revision purposes. Notwithstanding its many novel applications, LLM technology is inherently susceptible to bias and error. This poses a significant challenge in the educational setting, where academic integrity may be undermined. This study aims to evaluate the performance of the latest iteration of ChatGPT (ChatGPT4.0) in standardized state medical licensing examinations. Methods: A considered search strategy was used to interrogate the PubMed electronic database. The keywords ‘ChatGPT’ AND ‘medical education’ OR ‘medical school’ OR ‘medical licensing exam’ were used to identify relevant literature. The search included all peer-reviewed literature published in the past five years. The search was limited to publications in the English language only. Eligibility was ascertained based on the study title and abstract and confirmed by consulting the full-text document. Data was extracted into a Microsoft Excel document for analysis. Results: The search yielded 345 publications that were screened. 225 original articles were identified, of which 11 met the pre-determined criteria for inclusion in a narrative synthesis. These studies included performance assessments in national medical licensing examinations from the United States, United Kingdom, Saudi Arabia, Poland, Taiwan, Japan and Germany. ChatGPT 4.0 achieved scores ranging from 67.1 to 88.6 percent. The mean score across all studies was 82.49 percent (SD= 5.95). In all studies, ChatGPT exceeded the threshold for a passing grade in the corresponding exam. Conclusion: The capabilities of ChatGPT in standardized academic assessment in medicine are robust. While this technology can potentially revolutionize higher education, it also presents several challenges with which educators have not had to contend before. The overall strong performance of ChatGPT, as outlined above, may lend itself to unfair use (such as the plagiarism of deliverable coursework) and pose unforeseen ethical challenges (arising from algorithmic bias). Conversely, it highlights potential pitfalls if users assume LLM-generated content to be entirely accurate. In the aforementioned studies, ChatGPT exhibits a margin of error between 11.4 and 32.9 percent, which resonates strongly with concerns regarding the quality and veracity of LLM-generated content. It is imperative to highlight these limitations, particularly to students in the early stages of their education who are less likely to possess the requisite insight or knowledge to recognize errors, inaccuracies or false information. Educators must inform themselves of these emerging challenges to effectively address them and mitigate potential disruption in academic fora.

Keywords: artificial intelligence, ChatGPT, generative ai, large language models, licensing exam, medical education, medicine, university

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Authors: Tiffany Karalis Noel

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The purpose of this study was to examine the factors attributed to the self-efficacy of beginning secondary content area teachers as they moved through their student teaching experiences. This study used a narrative inquiry methodology to understand the variables attributed to teacher self-efficacy among a group of secondary content area teacher candidates. The primary purpose of using a narrative inquiry methodology was to share the stories of content area teacher candidates’ student teaching experiences. Focused research questions included: (1) To what extent does teacher education preparation affect the self-efficacy of beginning content area teachers? (2) Which recurrent elements of teacher education affect the self-efficacy of beginning teachers, regardless of content area? (3) How do the findings from research questions 1 and 2 inform teacher educators? The findings of this study suggest that teacher education preparation affects the self-efficacy of beginning secondary teacher candidates across the content areas; accordingly, the findings of this study provide insight for teacher educators to consider the areas where teacher education programs are failing to provide adequate preparation. These teacher candidates emphasized the value of adequate preparation throughout their teacher education programs to help inform their student teaching experiences. In order to feel effective and successful as beginning teachers, these teacher candidates required additional opportunities to apply the practical application of their teaching skills prior to the student teaching experience, the incorporation of classroom management strategy coursework into their curriculum, and opportunities to explore the extensive demands of the teaching profession ranging from time management to dealing with difficult parents, to name a few referenced examples. The teacher candidates experienced feelings of self-doubt related to their effectiveness as teachers when they were unable to employ successful classroom management strategies, pedagogical techniques, or even feel confidence in navigating challenging conversations with students, parents, and/or administrators. In order to help future teacher candidates and beginning teachers in general overcome these barriers, additional coursework, fieldwork, and practical application experiences should be provided in teacher education programs to help boost the self-efficacy of student teachers.

Keywords: self-efficacy, teacher efficacy, secondary preservice teacher education, teacher candidacy, student teaching

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Authors: Haohao Wang

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Technology, multimedia in Open Educational Resources, can contribute positively to student performance in an online instructional environment. Student performance data of past four years were obtained from an online course entitled Applied Calculus (MA139). This paper examined the data to determine whether multimedia (independent variable) had any impact on student performance (dependent variable) in online math learning, and how students felt about the value of the technology. Two groups of student data were analyzed, group 1 (control) from the online applied calculus course that did not use multimedia instructional materials, and group 2 (treatment) of the same online applied calculus course that used multimedia instructional materials. For the MA139 class, results indicate a statistically significant difference (p = .001) between the two groups, where group 1 had a final score mean of 56.36 (out of 100), group 2 of 70.68. Additionally, student testimonials were discussed in which students shared their experience in learning applied calculus online with multimedia instructional materials.

Keywords: online learning, open educational resources, multimedia, technology

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Authors: Joseph G. Grzywacz, Azza O. Abdelmoneium, Abdallah M. Badahdah

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Qatar provides an interesting context for studying family-friendly policies for working adults because it is governed by monarchy and Sharia law provides the foundation for legislation. This study considers gender variation in the perceived importance of and satisfaction with 12 family-friendly benefits (e.g., paid parental leave, flexible work hours, onsite childcare). Data were from a 2017 national sample of native Qatari adults. The perceived importance of family-friendly benefits was comparable for women and men for 7 of 12 benefits. Men rated paid paternity leave higher than women, but women rated telecommuting, dedicated breastfeeding room, onsite childcare, and opportunities to job share higher than men. Women and men reported similar experiences for 9 of 12 family-friendly benefits, but men’s expectations were better met than women’s for flexible work arrangements, telecommuting, and financial support for athletic clubs.

Keywords: culture, family-friendly benefits, gender, work-family

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Authors: Tanit Pruktara

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The research aimed to study the students’ learning achievement and awareness level on electrical energy consumption and conservation and also to investigate the students’ attitude on the developed multimedia supplemented instructional unit for learning household electrical energy consumption and conservation in grade 10 Thailand student. This study used a quantitative method using MCQ for pre and post-achievement tests and Likert scales for awareness and attitude survey questionnaires. The results from this were employed to improve the multimedia to be appropriate for the classroom and with real life situations in the second phase, the main study. The experimental results showed that the developed learning unit significantly improved the students’ learning achievement as well as their awareness of electric energy conservation. Additional we found the student will enjoy participating in class activities when the lessons are taught using multimedia and helps them to develop the relevance between the course and real world situations.

Keywords: lesson plan, media and animations, training course, vocational school in Thailand

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Authors: Peter I. I. Ikokwu

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Recent developments in the world, both in the health and education sectors, have further popularized the importance of Information and Communication Technology (ICT). ICT is available for many purposes, including teaching and learning, and its use in education is believed to empower both teachers and students by making the educational process more effective and interactive. The study examined the perceptions of teachers on the factors affecting the use of ICT in the teaching and learning of chemistry and the mitigating factors. The study involved all the lecturers (herein referred to as teachers) in the Colleges of Education in South Eastern Nigeria. The survey design was employed. 35 teachers were selected by stratified random sampling from about 78 chemistry teachers in these Colleges. However, 34 questionnaires were recovered, comprising 13 males and 21 females. 3 research questions and 3 hypotheses guided the study. Results show that the teachers have a clear perception of the factors militating against the use of ICT in the teaching and learning of chemistry, with a pooled mean of 2.96. But there was no significant difference in the perceptions of male and female teachers. Also, they identified the mitigating factors highlighted with no significant difference between the perceptions of the males and females with pooled means of 3.23 and 3.11, respectively. In all, it is noteworthy that lack of funds, irregular and inadequate power supply, and inadequate time in the school timetable was among the militating factors. Recommendations were made for the consideration of the government, the teachers, and the Institutions.

Keywords: chemistry, teachers, perception, ICT, learning

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Authors: Jui-Chien Wang

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Because of China’s continued economic growth and dominance, increasingly many students of Chinese from western countries are interested in pursuing careers related to China. Unless we do more to teach them about contemporary Chinese society and Chinese cultural codes, however, few will be able to do so successfully. Most traditional language textbooks treat these topics only cursorily, and, because of the rapid pace of China’s social and economic development, what they do cover is frequently outdated and insufficient. However, understanding contemporary Chinese society and Chinese cultural codes is essential to successfully negotiating real-world China. The current paper details one of the main ways in which the presenter has dealt with this educational lacuna: the development and implementation of a series of teaching modules for advanced Chinese language classes. Each module explores a particular area, provides resources, and raises questions to engage students in strengthening their language and cultural competencies. The teaching modules address four main areas: (1) Chinese behavioral culture; (2) critical issues in contemporary China; (3) current events in China; and (4) great social transformations in contemporary China. The presenter will also discuss lessons learned and insights gained during the development and implementation process as well as the benefits of using these modules. In addition, the presenter will offer suggestions for the application of these modules, so that other language teachers will be able to make better use of them in their own classrooms.

Keywords: behavioral culture, contemporary Chinese society, cultural code, teaching module

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Authors: Damian Gordon, Paul Doyle, Anna Becevel, Júlia Vilafranca Molero, Cinta Gascon, Arianna Vitiello, Tina Baloh

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It has long been recognized that the creation of any teaching content can be enhanced if the development process follows a pre-defined approach, which is often referred to as an instructional design methodology. These methodologies typically define a number of stages, or phases, that an educator should undertake to help ensure the quality of the final teaching content that is developed. In this paper, we present an instructional design methodology that is focused specifically on the introduction of blended resources into a heretofore bricks-and-mortar course. To achieve this, research was undertaken concerning a range of models of instructional design, as well as literature covering some of the key challenges and “pain points” of blending. Following this, our model, the BL-5D model, is presented, which incorporates some key questions at each stage of this five-stage methodology to guide the development process. Finally, a discussion of some of the key themes and issues that have been uncovered in this work is presented, as well as a template for a blended learning case study that emerged from this approach.

Keywords: blended learning, challenges of blended learning, design methodologies, instructional design

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Authors: Rim Bouhaouel, Naoufel Kraïem, Zuhoor Al Khanjari

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Unified Modeling Language (UML) is considered as one of the widespread modeling language standardized by the Object Management Group (OMG). Therefore, the model driving engineering (MDE) community attempts to provide reuse of UML diagrams, and do not construct it from scratch. The UML model appears according to a specific software development process. The existing method generation models focused on the different techniques of transformation without considering the development process. Our work aims to construct an UML component from fragments of UML diagram basing on an agile method. We define UML fragment as a portion of a UML diagram, which express a business target. To guide the generation of fragments of UML models using an agile process, we need a flexible approach, which adapts to the agile changes and covers all its activities. We use the software product line (SPL) to derive a fragment of process agile method. This paper explains our approach, named RECUP, to generate UML fragments following an agile process, and overviews the different aspects. In this paper, we present the approach and we define the different phases and artifacts.

Keywords: UML, component, fragment, agile, SPL

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Authors: Muhammad Muhammad Suleiman

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Cloud Computing is a versatile concept that refers to a service that allows users to outsource their data without having to worry about local storage issues. However, the most pressing issues to be addressed are maintaining a secure and reliable data repository rather than relying on untrustworthy service providers. In this study, we look at how stenography approaches and collaboration with Digital Watermarking can greatly improve the system's effectiveness and data security when used for Cloud Computing. The main requirement of such frameworks, where data is transferred or exchanged between servers and users, is safe data management in cloud environments. Steganography is the cloud is among the most effective methods for safe communication. Steganography is a method of writing coded messages in such a way that only the sender and recipient can safely interpret and display the information hidden in the communication channel. This study presents a new text steganography method for hiding a loaded hidden English text file in a cover English text file to ensure data protection in cloud computing. Data protection, data hiding capability, and time were all improved using the proposed technique.

Keywords: cloud computing, steganography, information hiding, cloud storage, security

Procedia PDF Downloads 174

Authors: Rita Garcia

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Intelligent Programming Tutors, IPTs, are supplemental educational devices that assist in teaching software development. These systems provide customized learning allowing the user to select the presentation pace, pedagogical strategy, and to recall previous and additional teaching materials reinforcing learning objectives. In addition, IPTs automatically records individual’s progress, providing feedback to the instructor and student. These tutoring systems have an advantage over Tutoring Systems because Intelligent Programming Tutors are not limited to one teaching strategy and can adjust when it detects the user struggling with a concept. The Intelligent Programming Tutor is a category of Intelligent Tutoring Systems, ITS. ITS are available for many fields in education, supporting different learning objectives and integrate into other learning tools, improving the student's learning experience. This study provides a comparison of the IPTs currently adopted by the educational community and will focus on the different teaching methodologies and programming languages. The study also includes the ability to integrate the IPT into other educational technologies, such as massive open online courses, MOOCs. The intention of this evaluation is to determine one system that would best serve in a larger ongoing research project and provide findings for other institutions looking to adopt an Intelligent Programming Tutor.

Keywords: computer education tools, integrated software development assistance, intelligent programming tutors, tutoring systems

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Authors: C. Vogeler

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Change processes such as individualization and digitalization have an impact on higher education. Graduates are expected to cooperate in creative work processes in their professional life. During their studies, they need to be prepared accordingly. This includes modern learning scenarios that integrate the benefits of digital media. Therefore, design thinking and blended learning have been combined in the project-based seminar conception introduced here. The presented seminar conception has been realized and evaluated with students of information sciences since September 2017. Within the seminar, the students learn to work on a project. They apply the methods in a problem-based learning scenario. Task of the case study is to arrange a conference on the topic gaming in libraries. In order to collaborative develop creative possibilities of realization within the group of students the design thinking method has been chosen. Design thinking is a method, used to create user-centric, problem-solving and need-driven innovation through creative collaboration in multidisciplinary teams. Central characteristics are the openness of this approach to work results and the visualization of ideas. This approach is now also accepted in the field of higher education. Especially in problem-based learning scenarios, the method offers clearly defined process steps for creative ideas and their realization. The creative process can be supported by digital media, such as search engines and tools for the documentation of brainstorming, creation of mind maps, project management etc. Because the students have to do two-thirds of the workload in their private study, design thinking has been combined with a blended learning approach. This supports students’ preparation and follow-up of the joint work in workshops (flipped classroom scenario) as well as the communication and collaboration during the entire project work phase. For this purpose, learning materials are provided on a Moodle-based learning platform as well as various tools that supported the design thinking process as described above. In this paper, the seminar conception with a combination of design thinking and blended learning is described and the potentials and limitations of the chosen strategy for the development of a course with a multimedia approach in higher education are reflected.

Keywords: blended learning, design thinking, digital media tools and methods, flipped classroom

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Authors: Muhammad Saleem

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The research paper entitled above would be a comprehensive and critical study of translations of the meanings of the Holy Qur’an. The discussion will deal with the targets & purposes of Urdu (National Language of Pakistan) translators of the meanings of the Holy Qur’an. There are more than 400 translations of the meanings of the Holy Qur’an in the Urdu Language. Muslims, non-Muslims and some organizations have made translations of the meanings of the Holy Qur’an to meet various targets. It is observed that all Urdu translators have not translated the Qur’an with a single objective and motivation; rather, some are biased and strive to discredit the Qur’an. Thus, they have made unauthentic and fabricated translations of the Qur’an. Some optimistically believe that they intend to do a service, whereas others pessimistically hold that they treacherously seek to further their rule. Some of them have been observed to be against Islam, starting their activities with spite, but after perceiving the truths of Islam and the miracle and greatness of the Holy Qur’an, they submitted to Islam, embracing it with pure hearts. Some translators made their translations of the meanings of the Holy Qur’an to serve Allah, and some of them have done their translations to earn only. All these translations vary from one to another due to style, trend, type, method and style. Some Urdu translations have been made to fulfill the lingual requirements. Some translations have been made by Muslim scholars to reduce the influence of Urdu translations of the meanings of the Holy Qur’an by Non-Muslims. The article deals with the various purposes of the translators of the meanings of the Holy Qur’an.

Keywords: Qur'an, translation, urdu, language

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Authors: Hailah Alballaa, Azeddine Chikh

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Several challenges impede the adoption of Recommender Systems for Technology Enhanced Learning (TEL): to collect and identify possible datasets; to select between different recommender approaches; to evaluate their performances. The aim is of this paper is twofold: First, it aims to introduce a survey on the most significant work in this area. Second, it aims at identifying possible research directions.

Keywords: datasets, content-based filtering, recommender systems, TEL

Procedia PDF Downloads 229

Authors: Sanele E. Nene L., Lia M. Hewitt

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Background: Artificial intelligence (AI) has a high value in healthcare, various applications have been developed for the efficiency of clinical operations, such as appointment/surgery scheduling, diagnostic image analysis, prognosis, prediction and management of specific ailments. Purpose: The purpose of this study was to explore, describe, contrast, evaluate, and develop the various leadership strategies as a conceptual framework, applied by public health Operational Managers (OMs) to embrace AI benefits, with the aim to improve the healthcare system in a public hospital. Design and Method: A qualitative, exploratory, descriptive and contextual research design was followed and a descriptive phenomenological approach. Five phases were followed to conduct this study. Phenomenological individual interviews and focus groups were used to collect data and a phenomenological thematic data analysis method was used. Findings and conclusion: Three themes surfaced as the experiences of AI by the OMs; Positive experiences related to AI, Management and leadership processes in AI facilitation, and Challenges related to AI.

Keywords: ambivalence, embracing, Artificial intelligence, public hospital

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Authors: Chhavi Rana

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The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

Procedia PDF Downloads 64