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![Figure 3. Learning Analytics Reference Model ( modified from [18])](profile/Ghassan-Shahin-3/publication/339055597/figure/fig2/AS:855366350929920@1580946638172/Learning-Analytics-Reference-Model-modified-from-18_Q320.jpg)
Abstract. Learning analytics has not been extensively used yet as necessary tools in the management and operation of public universities in Malaysia. Massive amount of data been created and collected on students at the faculty but mostly remain dark and unexplored. Generating many reports, having lots of alerts or dashboards does not make a faculty...
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... one domino is removed, it can be more difficult or impossible to achieve the desired value. Examining Chatti's [18] learning analytics reference model in Figure 3, there are four dimensions to look into which includes: types of data available, purpose of doing it, the analyst and beneficiaries involved and applying data science works to carry it out. ...
Citations
... Learning analytics has become a brand new field for identifying student behavior, academic performance, etc. Literature [23] reviewed research on analytics learning and found that the existing research results are not well developed and an overall lack of a review of learning analytics research from the perspective of the scope of learning analytics research. Data-driven requires teachers to have strong analytical skills, and literature [24] discusses the impact of Industry 4.0 on education, pointing out that colleges and universities must transform traditional teaching practices and change traditional teaching concepts in order to adapt to the development of the times. More and more digital systems have accelerated the development of learning analytics, and literature [25] demonstrates studies related to data-driven education and digital learning, which enrich the knowledge of instructional management in digitally mediated spaces. ...
In order to gain insights into the key factors affecting students’ success in online education, this paper extracts students’ online learning behavioral feature indicators through the behavioral record data in the online learning platform, applies the attribute approximation algorithm based on the Bayesian Fuzzy Rough Set (IDB-BRS) model to attribute approximation of the behavioral indicators, and utilizes the improved Apriori algorithm to mine the association rules between online learning behaviors and learning effects. The improved Apriori algorithm is used to establish association rules between online learning behaviors and learning effects. In comparison to the VPFRS model attribute approximation algorithm, the IDB-BRS model attribute approximation algorithm does not necessitate pre-given parameters and achieves superior classification accuracy and approximation time in the Soybean, Credit, and Balance datasets, thereby offering greater practical value. The association rules reveal that students who carefully study course resources, actively submit assignments, and study online frequently contribute positively to their success in online learning. This paper holds significant implications for enhancing the effectiveness of learning in online education.
... Education informatization has moved from "resource sharing" to a new stage of "data-driven", and in such an informatization environment, it is important to actively use the new generation of information technology to innovate the mechanism, innovate the governance, optimize the environment, reform the teaching and improve the evaluation, and promote the deep integration of information technology and pedagogical management, so as to make the education and learning management more efficient. The deep integration of pedagogical management so that information technology becomes the "engine" to promote the development of school characteristics is an important way to build the characteristics of ordinary higher vocational colleges and universities [3][4][5][6]. The key to the digital transformation of education and the development of school characteristics is to promote the digitalization of all elements, all operations, all fields, and all processes for the curriculum content, teaching mode, and evaluation method. ...
It is an indisputable fact that there exists a non-integrated phenomenon between educational theory and practice, and educational theory and practice have the correlation of both interdependence and mutual deviation, so how to make the two coordinate and harmonize the development of the two has become a hotspot of the current research. In this paper, under the perspective of evidence-based education, we explore the coordinated development of the two, construct the coordinated index system of theoretical and practical education, and utilize the entropy weighting method to assign weights to the index system. Subsequently, taking School A as an example, the data-driven web crawler technology was utilized to obtain the relevant data, and the internal coordination level and future development trend of its theoretical and practical education were measured by the linear weighting method, the coupled coordination degree model, and the GM(1,1) model, respectively. The results show that the coupling coordination degree of its theoretical and practical education increases from 0.1489 in 2013 to 0.5609 in 2022, and although it maintains an upward posture, the overall values are lower than 0.6, and the coupling coordination level is low. In addition, the level of theory and practice education coordination is showing a continuous upward trend, with an average growth rate of 15.19%, but has shown a slowing trend. The coordinated and healthy development of theory and practice education in higher vocational colleges and universities can be realized from three aspects: enriching the form of theory and practice education activities, strengthening the atmosphere of theory and practice education, and school-enterprise cooperation.
... The "Ministry of Higher Education" (MOHE) intends to emphasize LA to incorporate the learning and teaching transformation in higher education institutions, shifting the emphasis from retention to better satisfy the present changes in the industry known as the "fourth industrial revolution" (IR 4.0) [26]. To follow the IR 4.0 revolution, the MOHE advised a focus on four key areas: reforming learning classrooms, integrating 21stcentury teaching methods, utilizing a flexible curriculum to address new developments and fields of knowledge, and utilizing the most recent teaching and learning technologies [27]. Studies confirmed academics' high interest in utilizing LA for learning and teaching and its positive role in learners' performance in Malaysian higher learning institutions [23,26]. ...
... However, LA implementation in Malaysia is fraught with difficulties. As a vital tool for the management and operation of educational institutions in Malaysia, LA is still not frequently utilized [27]. There is a lack of theoretical models that examine the determinants of LA tools' usage in higher education in Malaysia. ...
Learning analytics (LA) is a rapidly growing educational technology with the potential to enhance teaching methods and boost student learning and achievement. Despite its potential, the adoption of LA remains limited within the education ecosystem, and users who do employ LA often struggle to engage with it effectively. As a result, this study developed and assessed a model for users’ intention to utilize LA dashboards. The model incorporates constructs from the “Unified Theory of Acceptance and Use of Technology”, supplemented with elements of personal innovativeness, information quality, and system quality. The study utilized exploratory research methodology and employed purposive sampling. Participants with prior experience in LA technologies were selected to take part in the study. Data were collected from 209 academic staff and university students in Malaysia (59.33% male) from four top Malaysian universities using various social networking platforms. The research employed “Partial Least Squares Structural Equation Modeling” to explore the interrelationships among the constructs within the model. The results revealed that information quality, social influence, performance expectancy, and system quality all positively impacted the intention to use LA. Additionally, personal innovativeness exhibited both direct and indirect positive impacts on the intention to use LA, mediated by performance expectancy. This study has the potential to offer valuable insights to educational institutions, policymakers, and service providers, assisting in the enhancement of LA adoption and usage. This study’s contributions extend beyond the present research and have the potential to positively impact the field of educational technology, paving the way for improved educational practices and outcomes through the thoughtful integration of LA tools. The incorporation of sustainability principles in the development and deployment of LA tools can significantly heighten their effectiveness, drive user adoption, and ultimately nurture sustainable educational practices and outcomes.
... When information technology spreads across all industries and has a revolutionary impact on the economy, business, governments, and countries, as well as society and humans, IR 4.0 emerges (Mokhtar et al., 2019). The revolution in technology is rapidly changing, producing new models and methods of education for the future and enhancing the universities' capabilities to prepare graduates for life in the real world (Rahardja et al., 2019). ...
... Teachers at secondary schools use innovative instructional strategies such as inquiry-and problem-based learning, which diverge from traditional educational norms. Fully student-centered learning remains a question mark as many teachers still practice traditional learning and are still less sensitive to the Industrial Revolution 4.0 challenges (Anealka, 2018;Mokhtar et al., 2019). This challenge must be overcome to produce active human capital. ...
... Role machines now, during the IR 4.0, carry out duties automatically to suit human demands via various systems, for instance, Internet of Things (IoT), Cyber-Physical System (CPS), IR 4.0, Advanced Management Program, or Industrial Internet (Kamaruddin & Che Aleha, 2016). In line with that, drastic changes in the integration of information technology should be given serious attention as it has a profound impact on the economy, business, government and country, society, and individuals (Mokhtar et al., 2019). If traced, this phenomenon affects the education system not only in the country but also in the world through the transformation of education at the global level in addressing the rapid pace of innovation and technology (Nurulrabihah et al., 2020) which is stated in PPPM (2013PPPM ( -2025 to produce students who are high-minded and competitive at the global level as well as enhance the STEM education quality. ...
... Analytics can recommend or create courses to students as per their skills and skills required trends. Multiple learning spaces can recommend transactions with any environment by identifying learner needs, directing learning and its records as a guide using Blockchain assurances on reliability in the certification [57,71,88]. ...
... The students can share a digital link to provide access to their profile to authorized people to not undermine confidentiality. Blockchain technology system ensures complete authenticity of credentials and student records as it has secured history of changes with signatures [23,53,71]. ...
... Some known use cases-Blockcerts: educational credentials management, APPII: CV builder to showcase verified educational merits to employers, ODEM: educational products and services marketplace, Sony Global Education: digital transcript management, BitDegree: online education platform with a gamified experience, Disciplina: individualized learning management system [65,71]. ...
The purpose of this paper is to portray an integrated Education Model which proposes to be student-centric and using technologies like Blockchain technology which can offer security, authenticity, immutability, longevity, data, information, decentralization, no intermediator, reliability, and data integrity. Education Blockchain 4.0 is a part of the Industrial revolution 4.0 which is disrupting the education sector. Blockchain technology is in focus in the last decade amongst users, researchers and practitioners. Despite the growing interest in Blockchain technology, the education sector has not seen much progress in terms of applications. This system uses the research of blockchain-based educational applications. The world is accelerating with IoT and AI. Industry revolution 4.0 is becoming a mandatory standard and necessity in globalization and a logical outcome is Education Blockchain 4.0. It is the need in the educational system due to the disruption initiated by technologies. Quantitative Methodology will be used to prove the conceptual model that proves the relationship between various constructs using PLS-SEM, which statistically can prove these relationships. Smart Blockchain System in education will make everything digital, easily storable, immutable, secured, longevity, easy access, cost-effective, User friendly and integrable to other technologies. The proposed Education Blockchain 4.0 smart system is poised to achieve these objectives across domains and to integrate further technologies making it the most suitable choice for these applications, which is the major contribution of this study. Also, the integration of the system theory of the new student centric education ecosystem, the disruption theory for Blockchain technology in education and the stakeholder theory applied to the education. Education Blockchain 4.0 will be useful for educational organizations to be cost-effective, achieve volumes of scale by integrating various functionalities, sectors. It will be useful for making policy decisions to enhance revenue, profitability and deliver stakeholder satisfaction.
... However, cutting edge technologies that apply advanced analytics techniques and tools are more promising to analyze a large volume of data extracted from digital online learning platforms. Therefore, as more analytics systems and tools become prevalent and accessible, these technologies can be used to leverage the large scale of data use in education, ranging from learning data collection, student learning process analysis, learning performance prediction, and making interventions to raise learning outcomes (Mokhtar et al., 2019). ...
... Munot et al. [22] explained the importance of dark data and discussed applications for its analysis. Mokhtar et al. [23] discussed the impact of IR 4.0 in education and research, and showed how a university could adapt to IR 4.0 and function in the big data environment by exploring dark data examples. Researchers have also discussed types of dark data by exploring hidden cybersecurity risks, and showed how companies can be proactive by managing the data in the right way to reduce those risks [24]. ...
The last decade has seen a rapid increase in big data, which has led to a need for more tools that can help organizations in their data management and decision making. Business intelligence tools have removed many of the obstacles to data visibility, and numerous data mining technologies are playing an essential role in this visibility. However, the increase in big data has also led to an increase in ‘dark data’, data that does not have any predefined structure and is not generated intentionally. In this paper, we show how dark data can be mined for practical purposes and utilized to gain business insight. The most common type of dark data is a log file generated on a web server. Using the example of log files generated by e-commerce transactions, this paper shows how residual data and data trails can prove to be valuable when an actual dataset is inaccessible, and explains the usage of residual data for modeling purposes. The work uses a system identification approach, based on natural language processing for log file tokenization and feature extraction. The features are then embedded into the next step, which uses a deep neural network to identify customers for targeted advertising. The results achieve a significant accuracy and show how dark data has the potential to deliver value for business. Locating, organizing, and understanding dark data can unlock its relevance, usefulness, and potential monetization, but it is important to act when the benefits of use outweigh the costs of access and analysis.
... Nowadays, the use of technology to enhance education and learning is rapidly expanding [4]. Intelligent Tutoring Systems (ITS) is one of the methods that represents a form of computer-based training using artificial intelligence techniques in which the system uses a knowledge base to give feedback to the student as the student interacts with the system. ...
E-learning platforms are becoming increasingly popular in the academic community due to various learning benefits achieved through learning without place or time limitations. Nowadays, the use of technology to enhance education and learning is rapidly expanding. By applying learning technologies like Intelligent Tutoring Systems, we can enhance the learning process. Intelligent Tutoring System is a technique that represents a form of computer-based training using artificial intelligence methods in which the system uses a knowledge base to give feedback to the learner as the student interacts with the system. Several techniques of knowledge representation are found in the literature. The most commonly used are Rule-based, Case-Based, Logic-based, Frame-based, Bayesian network, and Semantic-based. For the sake of learning personalization, ontologies have been used recently in learning systems as methods for knowledge representation. Ontology offers a shared vocabulary for domain modeling in which it shows the concepts present in the domain and their properties and relationships. The semantic modelling is typically based on an ontology that specifies classes and properties, i.e., the ontology determines the vocabulary to be used for the semantic description. Based on the overview of these techniques a semantic model for learning materials has been implemented using ontology. The built ontology can be applied in the future to control adaptive intelligent e-learning frameworks.
The trend of early retirement among teachers is worrisome as it results in the loss of experienced teachers and contributes to the expanding teacher shortage. This condition is thought to have been caused by the workload and strain connected with technology and data use. Early retirement signifies teachers’ burnout, stress, and lack of job satisfaction, and multiple studies have suggested that they significantly affect professional self-efficacy. This article explores the influence of technology and data use on a teacher's self-efficacy in relation to their profession. A total of 525 school teachers in Malaysia have participated in a research study by completing a questionnaire. The results indicated that teacher professional self-efficacy, technology use, and data use are at a moderate level. The findings demonstrate a favorable correlation among these constructs. However, the connections are not influenced by factors such as age, gender, or school location. The study's implications for the higher education setting are also addressed, suggesting the implementation of enhancement of professional development opportunities, mentorship and peer support, and recognition to teachers. These suggestions aim to better equip teachers with the indispensable skills required in the swiftly evolving realm of data and technology.
