2,501 research outputs found

    In their own voices: a nationwide study of students’ attitudes towards the implementation of smart learning environments in UAE schools

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    A smart learning environment (SLE) encompasses the use of advanced technology and smart pedagogical teaching skills tailored to suit students with diverse learning needs. In recent years, some countries, such as the United Arab Emirates (UAE), have formulated policies to implement SLE in their education systems. Since students are the intended beneficiaries of SLE policy, it is crucial to explore their perceptions of its implementation in a novel context. Therefore, this study explored the attitudes of students towards the implementation of SLE in the UAE. To conduct this investigation, 1857 secondary school students (grades 7 and 12) were recruited nationwide. A newly developed instrument was employed to collect data, which were then subjected to exploratory and confirmatory factor analyses to understand its dimensions and validate the factor structure, respectively. Subsequently, the mean scores were calculated and multivariate variance analysis, structural equation modelling, and moderation analysis were conducted to test three specific hypotheses. The results identified ambivalence among students regarding the implementation of SLE and significant differences between them based on their school location and study grade. Additionally, this study discussed the need for nationwide stakeholder engagement to deliberate on the scope, innovation of technological devices and necessary teacher development for efficient SLE implementation

    Organizing sustainable development

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    The role and meaning of sustainable development have been recognized in the scientific literature for decades. However, there has recently been a dynamic increase in interest in the subject, which results in numerous, in-depth scientific research and publications with an interdisciplinary dimension. This edited volume is a compendium of theoretical knowledge on sustainable development. The context analysed in the publication includes a multi-level and multi-aspect analysis starting from the historical and legal conditions, through elements of the macro level and the micro level, inside the organization. Organizing Sustainable Development offers a systematic and comprehensive theoretical analysis of sustainable development supplemented with practical examples, which will allow obtaining comprehensive knowledge about the meaning and its multi-context application in practice. It shows the latest state of knowledge on the topic and will be of interest to students at an advanced level, academics and reflective practitioners in the fields of sustainable development, management studies, organizational studies and corporate social responsibility

    New trends in research skills development of future teachers: quantitative approach and empirical studies

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    The purpose of this study was to investigate the relationship between the use of technology and the development of research skills in future teachers, specifically current graduate students participating in Mathematical Olympiads. The study used a quantitative approach and analyzed data collected through a survey. The findings indicated that quantitative analysis skills, problem-solving skills, communication skills, and research methodology skills are the dimensions of research skills in future teachers. The results showed that the use of digital tools for literature searching and curation, online courses and workshops (OCWs), collaborative learning and discussion forums, and data analysis software significantly and positively affected research skills. However, the study failed to provide evidence that digital portfolios (DPs) affected research skills. The limitations of the study and recommendations for future research are discussed. In conclusion, the findings highlight the importance of technology in the development of research skills in future teachers and suggest that technology-based learning resources and tools should be integrated into teacher training programs

    E-learning usage from a social constructivist learning approach: perspectives of Iraqi Kurdistan students in social studies classrooms

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    Background: Many schools in the Iraqi Kurdistan Region have incorporated information and communication technologies (ICT) into their environments. However, the results have shown that computer usage has had only a minimal effect on the classroom environment and learning outcomes. This minimal effect could be attributed to the teaching and learning of social studies subjects, which often rely on a traditional vision of teaching and an excessive inclusion of facts and dates in school textbooks. Consequently, students feel compelled to memorize all the information to pass tests. Yet, merely employing technology alongside traditional teaching and assessment approaches, such as lecturing or having students study in isolation without any form of collaborative learning, does not foster the development of students' higher-order thinking skills. It's time to revitalize school curricula and teaching practices to embrace a more contemporary, open-minded approach to social science education. This approach should incorporate a social constructivist perspective with technology to better instill international moral values such as democracy, respect for differences, and learning to live harmoniously with others. Aim: This cross-sectional study aims to investigate the impact of a social constructivist learning approach on the acceptance of technology and its influence on perceived e-learning outcomes among students in the Iraqi Kurdistan Region. Additionally, this study examines the differences in the effects of the social constructivist learning approach and dimensions of technology acceptance on perceived e-learning outcomes between students studying social studies in Arabic and those studying social studies in English. Setting and participants: Data were gathered from both public and private schools in Erbil governorate, situated in northern Iraq and affiliated with the Ministry of Education-Iraqi Kurdistan Regional Government. To select participants, a random sampling technique was employed, encompassing students in grades 8 through 12 of both genders. The data were obtained through a self-administered paper-based questionnaire. Instruments: Data were collected using a social constructivist learning environment survey (personal relevance, critical voice, shared control, uncertainty, student negotiation), dimensions of the attitude toward technology (attitude toward technology use, perceived usefulness, feeling ease of use, learning facility condition, and subjective norms), some additional external variables (investigation, respect for difference, student economic ability, and perceived e-learning outcomes), and socio-demographic data. Conclusion: This study is intended to emphasize the significance of employing constructivist pedagogy to enhance the technology acceptance model and improve learning outcomes. The findings of the study showed that a social constructivist learning environment had a favorable influence on perceived e-learning outcomes as well as ease of use, perceived usefulness, investigation, and respect for difference. Attitude towards technology use and perceived usefulness are contributory factors to the positive perceived e-learning outcomes. Furthermore, feeling ease of use technology has a positive effect on both attitude towards technology use and perceived usefulness. Perceived usefulness also has a direct positive impact on attitudes towards technology use. Finally, students’ technological experience is positively correlated with feeling ease of use but not with perceived usefulness. Additionally, regarding the comparison between students studying social studies in Arabic and those in English, the findings demonstrated that students studying social studies in English showed stronger positive effects from the social constructivist learning environment on their perceived e-learning outcomes. Conversely, students studying social studies in Arabic demonstrated a more potent positive effect of perceived usefulness on their attitudes towards technology. Moreover, the positive impact of an attitude towards technology use on perceived e-learning outcomes was more pronounced among the Arabic students compared to their English counterparts. Additionally, the influence of the learning facility on the perceived ease of use, as well as the perceived usefulness of technology, differed between the two groups. The English group experienced a more substantial positive impact. However, there was no significant difference observed in the effect of feeling ease of use on attitudes towards technology use between the English and Arabic student groups. Furthermore, no significant difference was observed in the effect of perceived usefulness on the social constructivist learning environment for either group. The findings from this research are expected to contribute to the development of effective and efficient counseling and support intervention programs. These programs can play a crucial role in transforming teachers

    Breaking Virtual Barriers : Investigating Virtual Reality for Enhanced Educational Engagement

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    Virtual reality (VR) is an innovative technology that has regained popularity in recent years. In the field of education, VR has been introduced as a tool to enhance learning experiences. This thesis presents an exploration of how VR is used from the context of educators and learners. The research employed a mixed-methods approach, including surveying and interviewing educators, and conducting empirical studies to examine engagement, usability, and user behaviour within VR. The results revealed educators are interested in using VR for a wide range of scenarios, including thought exercises, virtual field trips, and simulations. However, they face several barriers to incorporating VR into their practice, such as cost, lack of training, and technical challenges. A subsequent study found that virtual reality can no longer be assumed to be more engaging than desktop equivalents. This empirical study showed that engagement levels were similar in both VR and non-VR environments, suggesting that the novelty effect of VR may be less pronounced than previously assumed. A study against a VR mind mapping artifact, VERITAS, demonstrated that complex interactions are possible on low-cost VR devices, making VR accessible to educators and students. The analysis of user behaviour within this VR artifact showed that quantifiable strategies emerge, contributing to the understanding of how to design for collaborative VR experiences. This thesis provides insights into how the end-users in the education space perceive and use VR. The findings suggest that while educators are interested in using VR, they face barriers to adoption. The research highlights the need to design VR experiences, with understanding of existing pedagogy, that are engaging with careful thought applied to complex interactions, particularly for collaborative experiences. This research contributes to the understanding of the potential of VR in education and provides recommendations for educators and designers to enhance learning experiences using VR

    Predicting Paid Certification in Massive Open Online Courses

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    Massive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as “the Year of the MOOCs”, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5–13% and 1–3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learner’s activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learners’ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar iii iv classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learners’ discussion forums to predict paid certification after annotating learners’ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches – specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learners’ interaction in discussion forums can predict learners’ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learners’ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7

    Elevating Physical Education Teacher Through Technology Integration

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    This paper explores how the integration of educational technology (EdTech) has been incorporated into the field of physical education (PE) and explores the difficulties encountered by Physical Education teachers (PETs) in successfully implementing technology. Through a systematic secondary literature review spanning the past decade, this paper identifies several key challenges, including unpreparedness, a trial-and-error approach, a shift in teaching priorities, and variations in teacher effectiveness. These challenges underscore the pressing need for readiness, adaptability, and preparation in the face of unforeseen disruptions, particularly in the context of online instruction. This study presents substantial recommendations to address these issues, emphasizing the pivotal role of technology-infused teacher education programs and advocating for Physical Education teachers’ education (PETE) faculty to embrace technology leadership. By implementing these recommendations, PETE programs can better equip preservice teachers and faculty members to harness technology for more effective learning experiences. Consequently, this endeavor aims to elevate the quality of PE in our increasingly digitalized era

    Fairness-aware Machine Learning in Educational Data Mining

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    Fairness is an essential requirement of every educational system, which is reflected in a variety of educational activities. With the extensive use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in education, researchers and educators can analyze educational (big) data and propose new (technical) methods in order to support teachers, students, or administrators of (online) learning systems in the organization of teaching and learning. Educational data mining (EDM) is the result of the application and development of data mining (DM), and ML techniques to deal with educational problems, such as student performance prediction and student grouping. However, ML-based decisions in education can be based on protected attributes, such as race or gender, leading to discrimination of individual students or subgroups of students. Therefore, ensuring fairness in ML models also contributes to equity in educational systems. On the other hand, bias can also appear in the data obtained from learning environments. Hence, bias-aware exploratory educational data analysis is important to support unbiased decision-making in EDM. In this thesis, we address the aforementioned issues and propose methods that mitigate discriminatory outcomes of ML algorithms in EDM tasks. Specifically, we make the following contributions: We perform bias-aware exploratory analysis of educational datasets using Bayesian networks to identify the relationships among attributes in order to understand bias in the datasets. We focus the exploratory data analysis on features having a direct or indirect relationship with the protected attributes w.r.t. prediction outcomes. We perform a comprehensive evaluation of the sufficiency of various group fairness measures in predictive models for student performance prediction problems. A variety of experiments on various educational datasets with different fairness measures are performed to provide users with a broad view of unfairness from diverse aspects. We deal with the student grouping problem in collaborative learning. We introduce the fair-capacitated clustering problem that takes into account cluster fairness and cluster cardinalities. We propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain fair-capacitated clustering. We introduce the multi-fair capacitated (MFC) students-topics grouping problem that satisfies students' preferences while ensuring balanced group cardinalities and maximizing the diversity of members regarding the protected attribute. We propose three approaches: a greedy heuristic approach, a knapsack-based approach using vanilla maximal 0-1 knapsack formulation, and an MFC knapsack approach based on group fairness knapsack formulation. In short, the findings described in this thesis demonstrate the importance of fairness-aware ML in educational settings. We show that bias-aware data analysis, fairness measures, and fairness-aware ML models are essential aspects to ensure fairness in EDM and the educational environment.Ministry of Science and Culture of Lower Saxony/LernMINT/51410078/E

    A Critical Review Of Post-Secondary Education Writing During A 21st Century Education Revolution

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    Educational materials are effective instruments which provide information and report new discoveries uncovered by researchers in specific areas of academia. Higher education, like other education institutions, rely on instructional materials to inform its practice of educating adult learners. In post-secondary education, developmental English programs are tasked with meeting the needs of dynamic populations, thus there is a continuous need for research in this area to support its changing landscape. However, the majority of scholarly thought in this area centers on K-12 reading and writing. This paucity presents a phenomenon to the post-secondary community. This research study uses a qualitative content analysis to examine peer-reviewed journals from 2003-2017, developmental online websites, and a government issued document directed toward reforming post-secondary developmental education programs. These highly relevant sources aid educators in discovering informational support to apply best practices for student success. Developmental education serves the purpose of addressing literacy gaps for students transitioning to college-level work. The findings here illuminate the dearth of material offered to developmental educators. This study suggests the field of literacy research is fragmented and highlights an apparent blind spot in scholarly literature with regard to English writing instruction. This poses a quandary for post-secondary literacy researchers in the 21st century and establishes the necessity for the literacy research community to commit future scholarship toward equipping college educators teaching writing instruction to underprepared adult learners

    Three Studies of B2B Salespeople as Collectors of Competitive Intelligence

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    PhD thesis in Social SciencesB2B salespeople’s ability to collect competitive intelligence from the marketplace affects organizational and individual competitive advantage and, in turn, firm and salesperson performance. The collection, sharing and use of this information are of potential strategic interest, and individual information collection is an important part of market orientation. The complexity and rate of change of industrial markets are increasing due to factors such as rapid technological development, and firms need to adapt to shifting market conditions faster than ever before, heightening the need for CI collection. Boundary spanners like salespeople spend a large part of their time outside their organizations meeting customers and competitors and thus are in a unique position to collect information from the market. The overall objective of this thesis is to increase the understanding of different aspects of salesperson information collection. This is addressed through three subobjectives: one, investigating what motivates salespeople to collect information beyond factors with a direct effect; two, determining how information collection leads to salesperson learning in a digital setting; and three, identifying the types of information collected by salespeople and strategies for motivating salespeople to collect information needed by the organization. The main contribution of subobjective one is the finding that the effects of known drivers of motivation for collecting information may vary with the salesperson’s personality characteristics, which are represented here by the personality trait locus of control. This variation might explain, at least in part, why only a few salespeople consistently collect information, despite attempts to include all salespeople. The main contribution of subobjective two is the development of a theoretical framework for listening in a digital setting before meeting customers physically. A model of how social media affects salesperson learning and knowledge building is presented, thus adding to the growing effort to understand how salespeople can use social media to increase their knowledge from the information they collect. The main contribution of subobjective three is the finding that the information salespeople collect is tactical, for their own interest, and of little value to customers and the sales organization. To increase the value of the type of information salespeople collect, this thesis argues for a stronger focus on the relationship between sales managers and their salespeople. The use of sales managers as a motivational factor for collecting more specific information through the sales force has received scarce treatment in the literature on the motivation of salespeople to collect information
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