Search results for: institutional learning outcomes

Authors: Osama Shakeel

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The objective of the paper is to study the clinic-pathological factors, survival analyses, recurrence rate, metastatic rate, risk factors and the management of cutaneous malignant melanoma at Shaukat Khanum Memorial Cancer Hospital and Research Center. Methodology: From 2014 to 2017, all patients with a diagnosis of cutaneous malignant melanoma (CMM) were included in the study. Demographic variables were collected. Short and long term oncological outcomes were recorded. All data were entered and analyzed in SPSS version 21. Results: A total of 28 patients were included in the study. Median age was 46.5 +/-15.9 years. There were 16 male and 12 female patients. The family history of melanoma was present in 7.1% (n=2) of the patients. All patients had a mean survival of 13.43+/- 9.09 months. Lower limb was the commonest site among all which constitutes 46.4%(n=13). On histopathological analyses, ulceration was seen in 53.6% (n=15) patients. Unclassified tumor type was present in 75%(n=21) of the patients followed by nodular 21.4% (n=6) and superficial spreading 3.5%(n=1). Clark level IV was the commonest presentation constituting 46.4%(n=13). Metastases were seen in 50%(n=14) of the patients. Local recurrence was observed in 60.7%(n=17). 64.3%(n=18) lived after one year of treatment. Conclusion: CMM is a fatal disease. Although its disease of fair skin individuals, however, the incidence of CMM is also rising in this part of the world. Management includes early diagnoses and prompt management. However, mortality associated with this disease is still not favorable.

Keywords: malignant cancer of skin, cutaneous malignant melanoma, skin cancer, survival analyses

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Authors: Kaoutar Ben Azzou, Hanaa Talei

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Human resources is an important department in each organization as it manages the life cycle of employees from recruitment training to retirement or termination of contracts. The recruitment process starts with a job opening, followed by a selection of the best-fit candidates from all applicants. Matching the best profile for a job position requires a manual way of looking at many CVs, which requires hours of work that can sometimes lead to choosing not the best profile. The work presented in this paper aims at reducing the workload of HR personnel by automating the preliminary stages of the candidate screening process, thereby fostering a more streamlined recruitment workflow. This tool introduces an automated system designed to help with the recruitment process by scanning candidates' CVs, extracting pertinent features, and employing machine learning algorithms to decide the most fitting job profile for each candidate. Our work employs natural language processing (NLP) techniques to identify and extract key features from unstructured text extracted from a CV, such as education, work experience, and skills. Subsequently, the system utilizes these features to match candidates with job profiles, leveraging the power of classification algorithms.

Keywords: automated recruitment, candidate screening, machine learning, human resources management

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Suzi M. S. Cavalari, Tim Lewis

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Teletandem is a bilingual model of virtual exchange in which two partners from different countries( and speak different languages) meet synchronously and regularly over a period of 8 weeks to learn each other’s mother tongue (or the language of proficiency). At São Paulo State University (UNESP), participants should answer a questionnaire before starting the exchanges in which one of the questions refers to setting a goal to be accomplished with the help of the teletandem partner. In this context, the present presentation aims to examine the goal-setting activity of 79 Brazilians who participated in Portuguese-English teletandem exchanges over a period of four years (2012-2015). The theoretical background is based on goal setting and self-regulated learning theories that propose that appropriate efficient goals are focused on the learning process (not on the product) and are specific, proximal (short-term) and moderately difficult. The data set used was 79 initial questionnaires retrieved from the MulTeC (Multimodal Teletandem Corpus). Results show that only approximately 10% of goals can be considered appropriate. Features of these goals are described in relation to specificities of the teletandem context. Based on the results, three mechanisms that can help learners to set attainable goals are discussed.

Keywords: foreign language learning, goal setting, teletandem, virtual exchange

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Authors: Prasita Sooksamran, Wareerat Kaewurai

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STEM is an integrated teaching approach promoted by the Ministry of Education in Thailand. STEM Education is an integrated approach to teaching Science, Technology, Engineering, and Mathematics. It has been questioned by Thai teachers on the grounds of how to integrate STEM into the classroom. Therefore, the main objective of this study is to develop a science instructional model based on the STEM approach to enhance scientific mind and problem-solving skills for primary students. This study is participatory action research, and follows the following steps: 1) develop a model 2) seek the advice of experts regarding the teaching model. Developing the instructional model began with the collection and synthesis of information from relevant documents, related research and other sources in order to create prototype instructional model. 2) The examination of the validity and relevance of instructional model by a panel of nine experts. The findings were as follows: 1. The developed instructional model comprised of principles, objective, content, operational procedures and learning evaluation. There were 4 principles: 1) Learning based on the natural curiosity of primary school level children leading to knowledge inquiry, understanding and knowledge construction, 2) Learning based on the interrelation between people and environment, 3) Learning that is based on concrete learning experiences, exploration and the seeking of knowledge, 4) Learning based on the self-construction of knowledge, creativity, innovation and 5) relating their findings to real life and the solving of real-life problems. The objective of this construction model is to enhance scientific mind and problem-solving skills. Children will be evaluated according to their achievements. Lesson content is based on science as a core subject which is integrated with technology and mathematics at grade 6 level according to The Basic Education Core Curriculum 2008 guidelines. The operational procedures consisted of 6 steps: 1) Curiosity 2) Collection of data 3) Collaborative planning 4) Creativity and Innovation 5) Criticism and 6) Communication and Service. The learning evaluation is an authentic assessment based on continuous evaluation of all the material taught. 2. The experts agreed that the Science Instructional Model based on the STEM Education Approach had an excellent level of validity and relevance (4.67 S.D. 0.50).

Keywords: instructional model, STEM education, scientific mind, problem solving

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Authors: Beilei Xu, Wencheng Wu, Lei Lin, Rachel Melnyk, Ahmed Ghazi

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In operating rooms, excessive cognitive stress can impede the performance of a surgeon, while low engagement can lead to unavoidable mistakes due to complacency. As a consequence, there is a strong desire in the surgical community to be able to monitor and quantify the cognitive stress of a surgeon while performing surgical procedures. Quantitative cognitiveload-based feedback can also provide valuable insights during surgical training to optimize training efficiency and effectiveness. Various physiological measures have been evaluated for quantifying cognitive stress for different mental challenges. In this paper, we present a study using the cognitive stress measured by the task evoked pupillary response extracted from the time series eye-tracking measurements to predict task difficulties in a virtual reality based robotic surgery training environment. In particular, we proposed a differential-task-difficulty scale, utilized a comprehensive feature extraction approach, and implemented a multitask learning framework and compared the regression accuracy between the conventional single-task-based and three multitask approaches across subjects.

Keywords: surgical metric, task evoked pupillary response, multitask learning, TSFresh

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Authors: Konstantinos Kottikas, Vlasis Stathakopoulos, Ioannis G. Theodorakis, Efthymia Kottika

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Theorists have argued that Market Orientation is comprised of two facets, namely the Market Driven and the Market Driving components. The present theoretical paper centers on the latter, which to date has been notably under-investigated. The term Market Driving (MD) pertains to influencing the structure of the market, or the behavior of market players in a direction that enhances the competitive edge of the firm. Presently, the main objectives of the paper are the specification of key antecedents and outcomes of Market Driving behavior. Market Driving firms behave proactively, by leading their customers and changing the rules of the game rather than by responding passively to them. Leading scholars were the first to conceptually conceive the notion, followed by some qualitative studies and a limited number of quantitative publications. However, recently, academicians noted that research on the topic remains limited, expressing a strong necessity for further insights. Concerning the key antecedents, top management’s Transformational Leadership (i.e. the form of leadership which influences organizational members by aligning their values, goals and aspirations to facilitate value-consistent behaviors) is one of the key drivers of MD behavior. Moreover, scholars have linked the MD concept with Entrepreneurship. Finally, the role that Employee’s Creativity plays in the development of MD behavior has been theoretically exemplified by a stream of literature. With respect to the key outcomes, it has been demonstrated that MD Behavior positively triggers firm Performance, while theorists argue that it empowers the Competitive Advantage of the firm. Likewise, researchers explicate that MD Behavior produces Radical Innovation. In order to test the robustness of the proposed theoretical framework, a combination of qualitative and quantitative methods is proposed. In particular, the conduction of in-depth interviews with distinguished executives and academicians, accompanied with a large scale quantitative survey will be employed, in order to triangulate the empirical findings. Given that it triggers overall firm’s success, the MD concept is of high importance to managers. Managers can become aware that passively reacting to market conditions is no longer sufficient. On the contrary, behaving proactively, leading the market, and shaping its status quo are new innovative approaches that lead to a paramount competitive posture and Innovation outcomes. This study also exemplifies that managers can foster MD Behavior through Transformational Leadership, Entrepreneurship and recruitment of Creative Employees. To date, the majority of the publications on Market Orientation is unilaterally directed towards the responsive (i.e. the Market Driven) component. The present paper further builds on scholars’ exhortations, and investigates the Market Driving facet, ultimately aspiring to conceptually integrate the somehow fragmented scientific findings, in a holistic framework.

Keywords: entrepreneurial orientation, market driving behavior, market orientation

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Authors: Simao E. Luis

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From the 1960s to the 1990s, the social-psychological framework of language attitudes that emerged from the Canadian research tradition was very influential. Integrativeness was one of the main variables in Gardner’s theory because refugees and immigrants were motivated to learn English and French to integrate into the Canadian community. Second language (L2) scholars have expressed concerns over integrativeness because it cannot explain the motivation of L2 learners in global contexts. This study aims to investigate student motivation to learn English as a foreign language in Mozambique, and to contribute to the ongoing validation of the L2 Motivational Self System theory in an under-researched country. One hundred thirty-seven (N=137) university students completed a well-established motivation questionnaire. The data were analyzed with SPSS, and descriptive statistics, correlations, multiple regressions, and MANOVA were conducted. Results show that many variables contribute to motivated learning behavior, particularly the L2 learning experience and attitudes towards the English language. Statistically significant differences were found between males and females, with males expressing more motivation to learn the English language for personal interests. Statistically significant differences were found between older and younger students, with older students reporting more vivid images of themselves as future English language users. These findings have pedagogical implications because motivational strategies are positively correlated with student motivated learning behavior. Therefore, teachers should design L2 tasks that can help students to develop their future L2 selves.

Keywords: English as a foreign language, L2 motivational self system, Mozambique, university students

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Authors: Sadeq Al Yaari, Muhammad Alkhunayn, Ayman Al Yaari, Montaha Al Yaari, Adham Al Yaari, Sajedah Al Yaari, Fatehi Eissa

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Background & Aims: Audio-visual aids and computer-aided language instruction (CALI) have been documented to improve receptive skills, namely listening skills, in normal students. The increased listening has been attributed to the understanding of other interlocutors' speech, but recent experiments have suggested that audio-visual aids and CALI should be tested against the listening of students of special needs to see the effects of the former in the latter. This investigation described the effect of audio-visual aids and CALI on the performance of these students. Methods: Pre-and-posttests were administered to 40 students of special needs of both sexes at al-Malādh school for students of special needs aged between 8 and 18 years old. A comparison was held between this group of students and another similar group (control group). Whereas the former group underwent a listening course using audio-visual aids and CALI, the latter studied the same course with the same speech language therapist (SLT) with the classical method. The outcomes of the two tests for the two groups were qualitatively and quantitatively analyzed. Results: Significant improvement in the performance was found in the first group (treatment group) (posttest= 72.45% vs. pre-test= 25.55%) in comparison to the second (control) (posttest= 25.55% vs. pre-test= 23.72%). In comparison to the males’ scores, the scores of females are higher (1487 scores vs. 1411 scores). Suggested results support the necessity of the use of audio-visual aids and CALI in teaching listening at the schools of students of special needs.

Keywords: listening, receptive skills, audio-visual aids, CALI, special needs

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Authors: Jaspal Rana

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Oxidative stress has been identified as an underlying cause of ischemia-reperfusion (IR) related cognitive dysfunction and brain damage. Therefore, antioxidant based therapies to treat IR injury are being investigated. Allium cepa L. (onion) is used as culinary medicine and is documented to have marked antioxidant effects. Hence, the present study was designed to evaluate the effect of A. cepa outer scale extract (ACE) against IR induced cognition and biochemical deficit in mice. ACE was prepared by maceration with 70% methanol and fractionated into ethylacetate and aqueous fractions. Bilateral common carotid artery occlusion for 10 min followed by 24 h reperfusion was used to induce cerebral IR injury. Following IR injury, ACE (100 and 200 mg/kg) was administered orally to animals for 7 days once daily. Behavioral outcomes (memory and sensorimotor functions) were evaluated using Morris water maze and neurological severity score. Cerebral infarct size, brain thiobarbituric acid reactive species, reduced glutathione, and superoxide dismutase activity was also determined. Treatment with ACE significantly ameliorated IR mediated deterioration of memory and sensorimotor functions and rise in brain oxidative stress in animals. The results of the present investigation revealed that ACE improved functional outcomes after cerebral IR injury which may be attributed to its antioxidant properties.

Keywords: ischemia-reperfusion, neuroprotective, stroke, antioxidant

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

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Authors: Rui Wu

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In the volatile modern manufacturing environment, new orders randomly occur at any time, while the pre-emptive methods are infeasible. This leads to a real-time scheduling method that can produce a reasonably good schedule quickly. The dynamic Flexible Job Shop problem is an NP-hard scheduling problem that hybrid the dynamic Job Shop problem with the Parallel Machine problem. A Flexible Job Shop contains different work centres. Each work centre contains parallel machines that can process certain operations. Many algorithms, such as genetic algorithms or simulated annealing, have been proposed to solve the static Flexible Job Shop problems. However, the time efficiency of these methods is low, and these methods are not feasible in a dynamic scheduling problem. Therefore, a dynamic rule selection scheduling system based on the reinforcement learning method is proposed in this research, in which the dynamic Flexible Job Shop problem is divided into several parallel machine problems to decrease the complexity of the dynamic Flexible Job Shop problem. Firstly, the features of jobs, machines, work centres, and flexible job shops are selected to describe the status of the dynamic Flexible Job Shop problem at each decision point in each work centre. Secondly, a framework of reinforcement learning algorithm using a double-layer deep Q-learning network is applied to select proper composite dispatching rules based on the status of each work centre. Then, based on the selected composite dispatching rule, an available operation is selected from the waiting buffer and assigned to an available machine in each work centre. Finally, the proposed algorithm will be compared with well-known dispatching rules on objectives of mean tardiness, mean flow time, mean waiting time, or mean percentage of waiting time in the real-time Flexible Job Shop problem. The result of the simulations proved that the proposed framework has reasonable performance and time efficiency.

Keywords: dynamic scheduling problem, flexible job shop, dispatching rules, deep reinforcement learning

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Authors: Ghazala Kausar

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This study investigates Pakistani first year university students’ perception of the role of pragmatics in their general approach to learning English. The research is triggered by National Curriculum’s initiative to provide holistic opportunities to the students for language development and to equip them with competencies to use English language in academic and social contexts (New English National Curriculum for I-XII). The traditional grammar translation and examination oriented method is believed to reduce learners to silent listener (Zhang, 2008: Zhao 2009). This lead to the inability of the students to interpret discourse by relating utterances to their meaning, understanding the intentions of the users and how language is used in specific setting (Bachman & Palmer, 1996, 2010). Pragmatic competence is a neglected area as far as teaching and learning English in Pakistan is concerned. This study focuses on the different types of pragmatic knowledge, learners perception of such knowledge and learning strategies employed by different learners to process the learning in general and pragmatic in particular. This study employed three data collecting tools; a questionnaire, discourse completion task and interviews to elicit data from first year university students regarding their perception of pragmatic competence. Results showed that Pakistani first year university learners have limited pragmatic knowledge. Although they acknowledged the importance of linguistic knowledge for linguistic competence in the students but argued that insufficient English proficiency, limited knowledge of pragmatics, insufficient language material and tasks were major reasons of pragmatic failure.

Keywords: pragmatic competence, Pakistani college learners, linguistic competence

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Authors: Denise Levy, Anna Lucia C. H. Villavicencio

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In a rapid-changing world, science teachers face considerable challenges. In addition to the basic curriculum, there must be included several transversal themes, which demand creative and innovative strategies to be arranged and integrated to traditional disciplines. In Brazil, nuclear science is still a controversial theme, and teachers themselves seem to be unaware of the issue, most often perpetuating prejudice, errors and misconceptions. This article presents the authors’ experience in the development of an interdisciplinary pedagogical proposal to include nuclear science in the basic curriculum, in a transversal and integrating way. The methodology applied was based on the analysis of several normative documents that define the requirements of essential learning, competences and skills of basic education for all schools in Brazil. The didactic materials and resources were developed according to the best practices to improve learning processes privileging constructivist educational techniques, with emphasis on active learning process, collaborative learning and learning through research. The material consists of an illustrated book for students, a book for teachers and a manual with activities that can articulate nuclear science to different disciplines: Portuguese, mathematics, science, art, English, history and geography. The content counts on high scientific rigor and articulate nuclear technology with topics of interest to society in the most diverse spheres, such as food supply, public health, food safety and foreign trade. Moreover, this pedagogical proposal takes advantage of the potential value of digital technologies, implementing QR codes that excite and challenge students of all ages, improving interaction and engagement. The expected results include the education of the educators for nuclear science communication in a transversal and integrating way, demystifying nuclear technology in a contextualized and significant approach. It is expected that the interdisciplinary pedagogical proposal contributes to improving attitudes towards knowledge construction, privileging reconstructive questioning, fostering a culture of systematic curiosity and encouraging critical thinking skills.

Keywords: science education, interdisciplinary learning, nuclear science, scientific literacy

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Authors: Richard O’Riordan, Saritha Unnikrishnan

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Autonomous Vehicles (AVs) are incorporating an increasing number of ADAS features, including automated lane-keeping systems. In recent years, many research papers into lane detection algorithms have been published, varying from computer vision techniques to deep learning methods. The transition from lower levels of autonomy defined in the SAE framework and the progression to higher autonomy levels requires increasingly complex models and algorithms that must be highly reliable in their operation and functionality capacities. Furthermore, these algorithms have no room for error when operating at high levels of autonomy. Although the current research details existing computer vision and deep learning algorithms and their methodologies and individual results, the research also details challenges faced by the algorithms and the resources needed to operate, along with shortcomings experienced during their detection of lanes in certain weather and lighting conditions. This paper will explore these shortcomings and attempt to implement a lane detection algorithm that could be used to achieve improvements in AV lane detection systems. This paper uses a pre-trained LaneNet model to detect lane or non-lane pixels using binary segmentation as the base detection method using an existing dataset BDD100k followed by a custom dataset generated locally. The selected roads will be modern well-laid roads with up-to-date infrastructure and lane markings, while the second road network will be an older road with infrastructure and lane markings reflecting the road network's age. The performance of the proposed method will be evaluated on the custom dataset to compare its performance to the BDD100k dataset. In summary, this paper will use Transfer Learning to provide a fast and robust lane detection algorithm that can handle various road conditions and provide accurate lane detection.

Keywords: ADAS, autonomous vehicles, deep learning, LaneNet, lane detection

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Authors: Pei Wang

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The concept of social-emotional learning (SEL) is becoming increasingly popular in both research and practical applications worldwide. However, there is a lack of empirical studies and implementation of SEL in China, particularly from the perspective of parents. This qualitative study examined how Chinese parents perceived SEL, how their views on SEL were shaped, and how these views affected their decisions regarding their children’s education programs. Using the Collaborative for Academic Social and Emotional Learning Interactive Wheel framework and Bronfenbrenner's bioecological theory, the study conducted interviews with eight parents whose children attended public, international, and private schools in China. All collected data were conducted a thematic analysis involving three coding phases. The findings revealed that interviewees perceived SEL as significant to children’s development but held diverse understandings and perspectives on SEL at school depending on the amount and the quality of SEL resources available in their children’s schools. Additionally, parents’ attitudes towards the exam-oriented education system and Chinese culture influenced their views on SEL in school. Nevertheless, their socioeconomic status (SES) was the most significant factor in their perspectives on SEL, which significantly impacted their choices in their children's educational programs. High-SES families had more options to pursue SEL resources by sending their children to international schools or Western countries, while lower middle-class SES families had limited SEL resources in public schools. This highlighted educational inequality in China and emphasized the need for greater attention and investment in SEL programs in Chinese public schools.

Keywords: Chinese, inequality, parent, school, social-emotional learning

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Authors: Hexiong Liu

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Powder metallurgy is the optimal method for the consolidation and preparation of W(Mo) alloys, which exhibit excellent application prospects at high temperatures. The properties of W(Mo) alloys are closely related to the sintered density. However, controlling the sintered density and porosity of these alloys is still challenging. In the past, the regulation methods mainly focused on time-consuming and costly trial-and-error experiments. In this study, the sintering data for more than a dozen W(Mo) alloys constituted a small-scale dataset, including both solid and liquid phases of sintering. Furthermore, simple descriptors were used to predict the sintered density of W(Mo) alloys based on the descriptor selection strategy and machine learning method (ML), where the ML algorithm included the least absolute shrinkage and selection operator (Lasso) regression, k-nearest neighbor (k-NN), random forest (RF), and multi-layer perceptron (MLP). The results showed that the interpretable descriptors extracted by our proposed selection strategy and the MLP neural network achieved a high prediction accuracy (R>0.950). By further predicting the sintered density of W(Mo) alloys using different sintering processes, the error between the predicted and experimental values was less than 0.063, confirming the application potential of the model.

Keywords: sintered density, machine learning, interpretable descriptors, W(Mo) alloy

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Authors: Hamid Reza Behnood, Esmaeel Ayati, Tom Brijs, Mohammadali Pirayesh Neghab

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Investment projects in road safety planning can benefit from an effectiveness evaluation regarding their expected safety outcomes. The objective of this study is to develop a decision support system (DSS) to support policymakers in taking the right choice in road safety planning based on the efficiency of previously implemented safety measures in a set of regions in Iran. The measures considered for each region in the study include performance indicators about (1) police operations, (2) treated black spots, (3) freeway and highway facility supplies, (4) speed control cameras, (5) emergency medical services, and (6) road lighting projects. To this end, inefficiency measure is calculated, defined by the proportion of fatality rates in relation to the combined measure of road safety performance indicators (i.e., road safety measures) which should be minimized. The relative inefficiency for each region is modeled by the Data Envelopment Analysis (DEA) technique. In a next step, a fuzzy decision-making system is constructed to convert the information obtained from the DEA analysis into a rule-based system that can be used by policy makers to evaluate the expected outcomes of certain alternative investment strategies in road safety.

Keywords: performance indicators, road safety, decision support system, data envelopment analysis, fuzzy reasoning

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Authors: M. Nadeem, S. Nasir, K. A. Moazzam, R. Kashif

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The remarkable growth and evolution in information and communication technology (ICT) in the past few decades has transformed modern society in almost every aspect of life. The impact and application of ICT have been observed in almost all walks of life including science, arts, business, health, management, engineering, sports, and education. ICT in education is being used extensively for student learning, creativity, interaction, and knowledge sharing and as a valuable source of teaching instrument. Apart from the student’s perspective, it plays a vital role for teacher education, instructional methods and curriculum development. There is a significant difference in growth of ICT enabled education in developing countries compared to developed nations and according to research, this gap is widening. ICT gradually infiltrate in almost every aspect of life. It has a deep and profound impact on our social, economic, health, environment, development, work, learning, and education environments. ICT provides very effective and dominant tools for information and knowledge processing. It is firmly believed that the coming generation should be proficient and confident in the use of ICT to cope with the existing international standards. This is only possible if schools can provide basic ICT infrastructure to students and to develop an ICT-integrated curriculum which covers all aspects of learning and creativity in students. However, there is a digital divide and steps must be taken to reduce this digital divide considerably to have the profound impact of ICT in education all around the globe. This study is based on theoretical approach and an extensive literature review is being conducted to see the successful implementations of ICT integration in education and to identify technologies and models which have been used in education in developed countries. This paper deals with the modern applications of ICT in schools for both teachers and students to uplift the learning and creativity amongst the students. A brief history of technology in education is presented and discussed are some important ICT tools for both student and teacher’s perspective. Basic ICT-based infrastructure for academic institutions is presented. The overall conclusion leads to the positive impact of ICT in education by providing an interactive, collaborative and challenging environment to students and teachers for knowledge sharing, learning and critical thinking.

Keywords: information and communication technology, ICT, education, ICT infrastructure, learning

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Authors: Emily Cherono Sawe, Elisha Ondieki Makori

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Knowledge and digital economies have brought challenges and potential opportunities for universities to innovate and improve the quality of learning. Disruption technologies and information dynamics continue to transform and change the landscape in teaching, scholarship, and research activities across universities. Digital literacy is a fundamental and imperative element in higher education and training, as witnessed during the new norm. COVID-19 caused unprecedented disruption in universities, where teaching and learning depended on digital innovations and applications. Academic services and activities were provided online, including library information services. Information professionals were forced to adopt various digital platforms in order to provide information services to patrons. University libraries’ roles in fulfilling educational responsibilities continue to evolve in response to changes in pedagogy, technology, economy, society, policies, and strategies of parent institutions. Libraries are currently undergoing considerable transformational change as a result of the inclusion of a digital environment. Academic libraries have been at the forefront of providing online learning resources and online information services, as well as supporting students and staff to develop digital literacy skills via online courses, tutorials, and workshops. Digital literacy transformation and information staff are crucial elements reminiscent of the prioritization of skills and knowledge for lifelong learning. The purpose of this baseline research is to assess the implications of digital literacy transformation in institutions of higher learning in Kenya and share appropriate strategies to leverage and sustain teaching and research. Objectives include examining the leverage and preparedness of the digital literacy environment in streamlining learning in the universities, exploring and benchmarking imperative digital competence for information professionals, establishing the perception of information professionals towards digital literacy skills, and determining lessons, best practices, and strategies to accelerate digital literacy transformation for effective research and learning in the universities. The study will adopt a descriptive research design using questionnaires and document analysis as the instruments for data collection. The targeted population is librarians and information professionals, as well as academics in public and private universities teaching information literacy programmes. Data and information are to be collected through an online structured questionnaire and digital face-to-face interviews. Findings and results will provide promising lessons together with best practices and strategies to transform and change digital literacies in university libraries in Kenya.

Keywords: digital literacy, digital innovations, information professionals, librarians, higher education, university libraries, digital information literacy

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Authors: Vaibhavi Birle

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Posterior reversible encephalopathy syndrome is a rare clinic-radiological syndrome associated with acute changes in blood pressure during pregnancy. It is characterized symptomatically by headache, seizures, altered mental status, and visual blurring with radiological changes of white matter (vasogenic oedema) affecting the posterior occipital and parietal lobes of the brain. It is being increasingly recognized due to increased institutional deliveries and advances in imaging particularly magnetic resonance imaging (MRI). In spite of the increasing diagnosis the prediction of PRES and patient factors affecting susceptibility is still not clear. Hence, we conducted the retrospective study to analyse the factors associated with PRES at our tertiary centre.

Keywords: pres syndrome, eclampsia, maternal outcome, fetal outcome

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Authors: Bowen Ding, Yihao Kuang

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In recent years, with the development of large-scale pre-trained lan-guage models, building vector representations of text through deep neural network models has become a standard practice for natural language processing tasks. From the performance on downstream tasks, we can know that the text representation constructed by these models contains linguistic information, but its encoding mode and extent are unclear. In this work, a structural probe is proposed to detect whether the vector representation produced by a deep neural network is embedded with a syntax tree. The probe is trained with the deep metric learning method, so that the distance between word vectors in the metric space it defines encodes the distance of words on the syntax tree, and the norm of word vectors encodes the depth of words on the syntax tree. The experiment results on ELMo and BERT show that the syntax tree is encoded in their parameters and the word representations they produce.

Keywords: deep metric learning, syntax tree probing, natural language processing, word representations

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Authors: Mehmet Oren, Susan Pedersen, Sevket C. Cetin

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Despite the frequently criticized disadvantages of the traditional used paper and pencil assessment, it is the most frequently used method in our schools. Although assessments do an acceptable measurement, they are not capable of measuring all the aspects and the richness of learning and knowledge. Also, many assessments used in schools decontextualize the assessment from the learning, and they focus on learners’ standing on a particular topic but do not concentrate on how student learning changes over time. For these reasons, many scholars advocate that using simulations and games (S&G) as a tool for assessment has significant potentials to overcome the problems in traditionally used methods. S&G can benefit from the change in technology and provide a contextualized medium for assessment and teaching. Furthermore, S&G can serve as an instructional tool rather than a method to test students’ learning at a particular time point. To investigate the potentials of using educational games as an assessment and teaching tool, this study presents the implementation and the validation of an automated embedded assessment (AEA), which can constantly monitor student learning in the game and assess their performance without intervening their learning. The experiment was conducted on an undergraduate level engineering course (Digital Circuit Design) with 99 participant students over a period of five weeks in Spring 2016 school semester. The purpose of this research study is to examine if the proposed method of AEA is valid to assess student learning in a 3D Educational game and present the implementation steps. To address this question, this study inspects three aspects of the AEA for the validation. First, the evidence-centered design model was used to lay out the design and measurement steps of the assessment. Then, a confirmatory factor analysis was conducted to test if the assessment can measure the targeted latent constructs. Finally, the scores of the assessment were compared with an external measure (a validated test measuring student learning on digital circuit design) to evaluate the convergent validity of the assessment. The results of the confirmatory factor analysis showed that the fit of the model with three latent factors with one higher order factor was acceptable (RMSEA < 0.00, CFI =1, TLI=1.013, WRMR=0.390). All of the observed variables significantly loaded to the latent factors in the latent factor model. In the second analysis, a multiple regression analysis was used to test if the external measure significantly predicts students’ performance in the game. The results of the regression indicated the two predictors explained 36.3% of the variance (R2=.36, F(2,96)=27.42.56, p<.00). It was found that students’ posttest scores significantly predicted game performance (β = .60, p < .000). The statistical results of the analyses show that the AEA can distinctly measure three major components of the digital circuit design course. It was aimed that this study can help researchers understand how to design an AEA, and showcase an implementation by providing an example methodology to validate this type of assessment.

Keywords: educational video games, automated embedded assessment, assessment validation, game-based assessment, assessment design

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Authors: Masood Varshosaz, Kamyar Hasanpour

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In recent years, we have seen growing interest in recognizing humans in drone images for post-disaster search and rescue operations. Deep learning algorithms have shown great promise in this area, but they often require large amounts of labeled data to train the models. To keep the data acquisition cost low, augmentation techniques can be used to create additional data from existing images. There are many techniques of such that can help generate variations of an original image to improve the performance of deep learning algorithms. While data augmentation is potentially assumed to improve the accuracy and robustness of the models, it is important to ensure that the performance gains are not outweighed by the additional computational cost or complexity of implementing the techniques. To this end, it is important to evaluate the impact of data augmentation on the performance of the deep learning models. In this paper, we evaluated the most currently available 2D data augmentation techniques on a standard convolutional network which was trained for recognizing humans in drone images. The techniques include rotation, scaling, random cropping, flipping, shifting, and their combination. The results showed that the augmented models perform 1-3% better compared to a base network. However, as the augmented images only contain the human parts already visible in the original images, a new data augmentation approach is needed to include the invisible parts of the human body. Thus, we suggest a new method that employs simulated 3D human models to generate new data for training the network.

Keywords: human recognition, deep learning, drones, disaster mitigation

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Authors: Daniel Pyle, Nicole Pyle, Ben Lignugaris-Kraft, Lawrence Maheady

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Students with disabilities are not included in general education at the same rate as their peers without disabilities. There are multiple reasons cited for why inclusion rates vary, such as teachers' lack of knowledge of the successful delivery of inclusive practices to students with the most extensive support needs. However, decades of research document effective inclusive practices associated with benefits across domains for students with disabilities. One effective inclusive practice that teachers use to improve outcomes for students with disabilities is flexible grouping. Teachers can use flexible grouping to facilitate students working collaboratively by using peer-mediated interventions (PMIs). This article describes PMIs as a flexible grouping of High Leverage Practices (HLP). There are variations of PMIs to select from when using flexible grouping. PMIs are described by varied grouping arrangements and different instructional procedures to clarify the flexibility of grouping students and students’ roles within those groupings. In support of teachers’ use of flexible grouping in inclusive general education classrooms, we identify different PMI formats teachers can use depending on the preferred grouping arrangement, explain the distinctive characteristics of PMI models to distinguish expected procedures with peers, highlight outcomes associated with PMIs, and provide an overview of evaluating PMIs effectiveness.

Keywords: peer-mediated interventions, high leverage practices, flexible grouping, general education, special education

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Authors: C. von Essen

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This study outlines and evaluates a constructivist design approach to the creation of educational video on postgraduate business degree programmes. Many online courses are tapping into the educational affordances of video, as this form of online learning has the potential to create rich, multimodal experiences. And yet, in many learning contexts video is still being used to transmit instruction to passive learners, rather than promote learner engagement and knowledge creation. Constructivism posits the notion that learning is shaped as students make connections between their experiences and ideas. This paper pivots on the following research question: how can we design educational video in ways which promote constructivist learning and stimulate analytic viewing? By exploring and categorizing over two thousand educational videos created since 2014 for over thirty postgraduate courses in business, economics, mathematics and statistics, this paper presents and critically reflects on a taxonomy of video styles and features. It links the pedagogical intent of video – be it concept explanation, skill demonstration, feedback, real-world application of ideas, community creation, or the cultivation of course narrative – to specific presentational characteristics such as visual effects including diagrammatic and real-life graphics and aminations, commentary and sound options, chronological sequencing, interactive elements, and presenter set-up. The findings of this study inform a framework which captures the pedagogical, technological and production considerations instructional designers and educational media specialists should be conscious of when planning and preparing the video. More broadly, the paper demonstrates how learning theory and technology can coalesce to produce informed and pedagogical grounded instructional design choices. This paper reveals how crafting video in a more conscious and critical manner can produce powerful, new educational design.

Keywords: educational video, constructivism, instructional design, business education

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Authors: Adejoke Yemisi Ige

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This study presents a conceptual analysis and investigation into the development of a systematic framework required for better understanding of the social process of Alternative Dispute Resolution (ADR) and collective conciliation. The critical examination presented in this study is significant because; it draws on insight from ADR, negotiation and collective bargaining literature and applies it in our advancement of a methodical outline which gives an insight into the influence of the key actors and other stakeholder strategies and behaviours during dispute resolution in relation to the outcomes which is novel. This study is qualitative and essentially inductive in nature. One of the findings of the study confirms the need to consider ADR and collective conciliation within the context of the characteristic conditions; which focus on the need for some agreement to be reached. Another finding of the study shows the extent which information-sharing, willingness of the parties to negotiate and make concession assist both parties to attain resolution. This paper recommends that in order to overcome deadlock and attain acceptable outcomes at the end of ADR and collective conciliation, the importance of information exchange and sustenance of trade union and management relationship cannot be understated. The need for trade unions and management, the representatives to achieve their expectations in order to build the confidence and assurance of their respective constituents is essential. In conclusion, the analysis presented in this study points towards a set of factors that together can be called the social process of collective conciliation nevertheless; it acknowledges that its application to collective conciliation is new.

Keywords: alternative dispute resolution, collective conciliation, social process, theoretical framework, unveiling

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Authors: Jiajia Peng, Jianqing Qiu, Jianjun Ren, Yu Zhao

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Background: We aimed to compare clinical and survival differences between B-cell (B-NHL) and NKT-cell non-Hodgkin lymphomas (NKT-NHL) located in the nasal cavity, nasopharynx and paranasal sinuses, which are always categorized as one sinonasal type. Methods: Patients diagnosed with primary B-NHL and NKT-NHL in the nasal cavity, nasopharynx, and paranasal sinuses from the SEER database were included. We identified these patients based on histological types and anatomical sites and subsequently conducted univariate and multivariate Cox regression and Kaplan–Meier analyses to examine cancer-special survival (CSS) outcomes. Results: Overall, most B-NHL cases originated from the nasopharynx, while the majority of NKT-NHL cases occurred in the nasal cavity. Notably, the CSS outcomes improved significantly in all sinonasal B-NHL cases over time, whereas no such improvement trend was observed in each sinonasal NKT-NHL type. Additionally, increasing age was linked with an elevated risk of death in B-NHL, particularly in the nasal cavity (HR:3.37), rather than in NKT-NHL. Compared with B-NHL, the adverse effect of the higher stage on CSS was more evident in NKT-NHL, particularly in its nasopharynx site (HR: 5.12). Furthermore, radiotherapy was beneficial for survival in patients with sinonasal B-NHL and NKT-NHL, except in those with NKT-NHL in the nasopharynx site. However, chemotherapy has only been beneficial for CSS in patients with B-NHL in paranasal sinuses (HR: 0.42) since 2010, rather than in other types of B-NHL or NKT-NHL. Conclusions: Although B-NHL and NKT-NHL in the nasal cavity, nasopharynx and paranasal sinuses have similar anatomical locations, their clinic demographics and prognoses are largely different and should be treated and studied as distinct diseases.

Keywords: B-cell non-Hodgkin lymphomas, NKT-cell non-Hodgkin lymphomas, nasal cavity lymphomas, nasal sinuses lymphomas, nasopharynx lymphomas

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: Amorn Kritsanaphan

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This paper illustrates the decentralization of cultural heritage management in local Thailand, a place similar to other middle- income developing countries characterized by rapid tourism-industrialization, weakness formal state institutions and procedures, and intensity use of the cultural heritage resources. The author conducted field research in local Thailand, principally using qualitative primary data gathering. These were combined with records reviews and content analysis of documents. The author also attended local public meetings, and social activities, and interacted casually with local residents and governments. Cultural heritage management has been supposed to improve through multi-stakeholder participation and decentralization. However, processes and outcomes are far from being straightforward and depend on a variety of contingencies and contexts involved. Multi-stakeholder and participatory approach in decentralization of the cultural heritage management in Thailand have pushed to the forefront and sharpened a number of existing problems. However, under the decentralization, the most significant contribution has been in creating real political space where various local stakeholders have become active, respond and address their concerns in various ways vis-à-vis cultural heritage problems. Improving cultural heritage sustainability and viability of local livelihoods through decentralization and participatory approach is by no means certain. However, the shift instead creates spaces potent with possibilities for a meaningful and constructive engagement between and among local state and non-state actors that can lead to synergies and positive outcomes.

Keywords: decentralization, participatory approach, cultural heritage management, multi-stakeholder approach

Procedia PDF Downloads 142