Integrating Learning Analytics for higher-order thinking e-assessments

Business Intelligence (BI) represents a pivotal advancement in leveraging information technology to enhance organizational performance. BI tools serve as crucial aids in decision-making processes by furnishing requisite insights. In higher education institutions, BI can contribute to leaders and managers in providing perspectives related to academics, learning, and management. Central to BI development is the meticulous gathering of requirements, a process pivotal in identifying organizational informational and knowledge needs. This involves employing various methods such as interviews, observation, and analysis, including leveraging learning analytics to discern data utility for enhanced learning processes. Various studies show that learning analytics contributes to improving the learning and education process. On the other hand, learning analytics requires activity data that is integrated, subject oriented, and time series which are aligned with the characteristics of the data warehouse (DWH) as the main component of BI. This research endeavors to develop BI utilizing academic and e-learning data, exemplified through a case study of Telkom University's Academic Systems and Learning Management Systems (LMS). This study aims to provide actionable insights into the intersection of BI and learning analytics, ultimately enhancing educational processes and organizational decision-making capabilities. By integrating learning analytics into BI development, the resultant BI systems can cater not only to current managerial demands but also anticipate future analytical needs. The implementation of the multidimensional schema was successfully executed. This process involved mapping data from the academic information system and the LMS as data sources to the data warehouse, the Extract, Transform, and Load (ETL) process, and development of the prototype. The testing on the prototype indicated that the prototype meets the intended requirements and provides valuable insights through its comprehensive reporting capabilities. This demonstrates the effectiveness of the implemented multidimensional schema, ETL process, and the overall design of the reporting dashboard.

We implemented a systematic literature review to investigate the trends and issues of learning analytics in seamless learning environments. We collected and analyzed a total of 27 empirical journal articles that study and discuss learning analytics design and implementation in seamless learning environments. In a recent decade, researchers have explored various seamless learning environments supporting students’ inquiry-based and experiential learning. Seamless learning aims to engage students to develop, elaborate, and apply knowledge to various inquiry-based learning contexts. With the significance of formative assessment in digital learning environments, integrating learning analytics into seamless learning environments increasingly evolved to trace and assess student learning. Despite various uses of learning analytics during seamless learning, there is a lack of comprehensive reviews that illustrate how learning analytics has been integrated into seamless learning. This literature synthesis decomposes the collected journal articles to understand learning analytics design and integrations inherent to seamless learning environments. Also, using qualitative thematic analysis, this systematic literature review demonstrates various design practices and future research directions of learning analytics in seamless learning environments.

Tremendous proliferation in data generation in the past few years has paved the way for new research and the development of new and improved techniques and algorithms in different fields of science and education. Initially terms like educational data mining emerged as a branch of data mining borrowing techniques from its ancestor. The challenges brought about by this large and heterogeneous data are diverse and needs a greater serious technical treatment. New and emerging fields like learning analytics have been introduced to manage the complexities of this data deluge. Learning analytics deals with data in the context of learner and the learning environment to improve the overall learning experience. The ultimate aim of the field is to make use of the data about learners and their environments to gain insights into the learning process using some of the well-known techniques and algorithms from the fields of data mining and machine learning. The process involves collecting, analysis of data and reporting the results to understand and optimize the learning experience. The fields of data mining and academic analytics closely related to learning analytics. Systematic Literature Review (SLR) is a robust, organized and rigorous literature review and reporting process aimed at identifying, collecting and synthesizing the relevant literature on a research question according to specified criteria. The process is more unbiased and balanced by systematic sequence of steps. This paper presents a systematic literature review by first developing the systematic literature review protocol and then discussing the main findings of the literature review by especially focusing on the applications and uses of machine learning and data mining techniques in the domain of learning analytics.
 
 Index Terms—Systematic Literature Review (SLR), Learning Analytics (LA), Big Data, Educational Data Mining (EDM), Machine Learning (ML).

Learning Analytics is a new field of research that appears as a link between educator data and students. The Learning Analytics is also able to provide information about decision making to understand and optimise the learning process. Early childhood education is believed to be very important because the learner is open and always tries new things and is considered very meaningful for future processes in the development of all aspects of their personality. In this study, we aimed at investigating the application of learning analytics and how the learning process on child development in early childhood education. The Article Search Process is carried out on two databases, ScienceDirect and IEEE. In this study, the most important keywords are Learning Analytics and early childhood. The results of the search are 45 articles: (31/45) ScienceDirect and (14/45) IEEE, from 2012 to 2017. They are thoroughly explored in the Learning Analytics process, data collection and pre-processing, analysis and action, and post- processing. The process of data collection is done by implementing Online Systems or Game-Based Learning: 58% e-Learning systems, 27% Learning Analytics systems, and 15% Game-based learning. Many research was conducted on samples from Post graduate, High school and Elementary school. The results showed that early childhood education had the advantage of the use of the new technology and in enchancing the child’s knowledge and skills. Such as creativity and logical intelligence in the introduction of shapes and numbers. In the further study, the concept of Learning Analytics in the form of assessment and feedback that is given to support the improvement of the objectivity in the learning process by collaborating with educational games which can be another beneficial to the early childhood education. Objective assessment and feedback may be the monitor and prediction which will also be analised for the efficiency and effectiveness in the learning proces through the use ofthe technology.

A key notion conveyed by those who advocate for the use of data to enhance instruction is an awareness that learning analytics has the potential to improve instruction and learning but is not currently reaching that potential. Gibbons (2014) suggested that a lack of learning facilitated by current technology-enabled instructional systems may be due in part to the natural tendency of many designers to focus on the surface layers of instruction (i.e., the content and control layers), while failing to adequately design the internal (less visible) aspects (e.g., the data management layer). In this paper, we outline phases in the design process related to the data management layer that should be considered when integrating learning analytics into a technology-enabled learning system.

This paper presents a comprehensive review of the integration of Learning Analytics (LA), Artificial Intelligence (AI), and STEM education within classroom settings, aimed at enhancing educational outcomes. By examining the synergistic effects and interactions among LA, AI, and STEM disciplines, this review highlights how these technologies can collectively transform educational practices. It discusses the potential of LA and AI to personalize learning experiences, thereby improving engagement and academic success in STEM subjects. The paper also explores various case studies and success stories, illustrating practical implementations and the significant impact these technologies have made in schools. Additionally, it addresses the challenges and considerations related to the ethical use of AI and data privacy, providing insights into how educators and policymakers can navigate these issues. Overall, this review underscores the critical role of technology in shaping the future of education by fostering more adaptive and inclusive learning environments.

During the last few years, learning analytics (LA) has gained the interest of researchers in the field of education. Generally, LA is related with the development of methods that use educational data sets to support the learning process. Therefore, there is a need understanding how learners, educators, and institutions can best support this process. Thus, a framework is presented that tries to extend the collaborative three-dimensional virtual environments for educational by integrating a LA tool. The aim is to help the teacher to monitor and evaluate the students' learning process in these types of environments. It is the intention include a (1) comprehensive analysis of the currently available LA tools for educational, (2) design of a user-centered framework based the requirements gathered from the analysis, and (3) thorough evaluation of framework to allow identify possible behavior patterns of students within the environment, related to your preferences for materials and expertise.

Mathematics is important for the academic life of students. The Fourth Industrial Revolution has transformed the Mathematics education landscape and how students learn mathematics. As such, students today use ICT as a tool to help them understand mathematics in easy way. This is because ICT has an increasing influence on the way students interact, learn and live. The advancement of technology has triggered a revolution in data generation by the devices used. Hence, learning analytics (LA) is very useful and significant in today's teaching and learning. LA is a fast growing area in academia and helps teacher on collecting, analysis, processing and visualization of data to help them understand students in deep. This study aimed at identifying the impact of LA in mathematics curriculum. Hence, this article was drawn from previous works of literature to create an overview of the issue pertaining to LA on primary level mathematics curriculum. The application of LA in Mathematics is effective in helping teachers to understand students' potential thus improving the overall quality of the teaching and learning process. The application of technology within this approach will enable teachers to build an effective learning process. In future, the application of the LA and GBL approaches in Mathematics is an approach that can be explored in-depth to assess its effectiveness to improve students' mastery in Mathematics.

The effectiveness of using learning analytics for learning design primarily depends upon two concepts: grounding and alignment. This is the primary conjecture for the study described in this paper. In our design-based research study, we design, test, and evaluate teacher-facing learning analytics for an online inquiry science unit on global climate change. We design our learning analytics in accordance with a socioconstructivism-based pedagogical framework, called Knowledge Integration, and the principles of learning analytics Implementation Design. Our methodology for the design process draws upon the principle of the Orchestrating for Learning Analytics framework to engage stakeholders (i.e. teachers, researchers, and developers). The resulting learning analytics were aligned to unit activities that engaged students in key aspects of the knowledge integration process. They provided teachers with actionable insight into their students' understanding at critical junctures in the learning process. We demonstrate the efficacy of the learning analytics in supporting the optimization of the unit's learning design. We conclude by synthesizing the principles that guided our design process into a framework for developing and evaluating learning analytics for learning design.

Aligning objectives with assessment in online courses: Integrating learning analytics and measurement theory

The purpose of the article is to analyse the needs of general education schoolteachers’ data literacy skills that are important for the effective use of learning analytics in the teaching-learning process. The theoretical part of the article presents the idea of big data in education, highlights the aspects of pedagogical value of learning analytics technologies, provides the overview of learning analytic tools. Some overview and comparison of spread of learning analytics tools in general education schools in Lithuania and Germany is presented in the context of data-driven education. The empirical part of the article presents some results from a big qualitative study of teachers’ experiences applying learning analytics tools in teaching - learning process. The main question of the current research is what data literacy skills teachers need in order to use learning analytics tools and make data based pedagogical decisions. Semi-structured interviews were conducted with 10 Lithuanian and 9 German teachers from general education schools, who already have had experience in working with learning experience platforms (digital learning platforms that integrate learning analytics tools). Interview data were analysed by means of content analysis. The results of the qualitative study showed that in order to use learning analytics tools it is important for teachers to have such skills as: digital literacy, data collection, data analysis and interpretation, etc. Comparative analysis of informants’ answers showed that teachers in Lithuania and Germany expressed similar needs for data literacy skills.

This paper introduces a randomized study conducted among a group of 48 student Java programmers to assess the impact of learning analytics (LA) on their academic performance. The LA system design incorporated both cognitive and metacognitive tools to help learners take possession of their learning processes. Participation was voluntary and data about potential confounding factors were also collected to minimize bias by blocking on two or more factors (future work). This paper summarily explores the relationships between students' programming expertise, coding assignments, user experience and satisfaction, and academic performance. The results of this preliminary exploration are inconclusive as to whether the LA system made a difference in academic performance. Nevertheless, they seem to indicate that LA was beneficial to student programmers and that summative measures such as grades are not a proper metric to measure the usefulness of LA systems.

The analysis and use of data generated by students‘ interactions with learning systems or programs – learning analytics – has recently gained widespread attention in the educational technology community. Part of the reason for this interest is based on the potential of learning analytic techniques such as data mining to find hidden patterns in students‘ online interactions that can be meaningfully interpreted and then fed back to students in a way that supports their learning. In this paper we present an investigation of how the digital data records of students‘ interactions within an immersive 3D environment can be mined, modeled and analysed in real-time, to provide formative feedback to students as they complete simulated surgical tasks. The issues that emerged in this investigation as well as areas for further research and development are discussed.

With the arrival of 'big data; in education, the potential was recognised for learning analytics to track students' learning, to reveal patterns in their learning, or to identify at-risk students, in addition to guiding reform and supporting educators in improving teaching and learning processes [1]. Learning Analytics dashboards have been used at all levels, including institutional, regional and national level [2]. In classroom use, while learning visualisations are often based on counts of activity data or interaction patterns, there is increasing recognition that learning analytics relate to learning, and should therefore provide pedagogically useful information [3]. While increasing numbers of technology-enhanced learning applications are embracing the potential of learning analytics at the classroom level, often these are aimed at teachers. However, learners can also benefit from learning analytics data (e.g. [4][5]).

In this paper we propose an overview of Learning Analytics approaches in the context of Open education in its most recent developments related to Massive Open Online Courses (MOOCs) and Linked Data (LD), both strictly interconnected to the Open Educational Resources (OERs) concept. The MOOCs have developed as a seamless integration of different solutions and services which have appeared on the Web in recent years to enable the opening of courses to hundreds or thousands of participants on the Internet (Siemens 2013). Actually MOOCs combine, amongst others, the networking potentials offered by the social Web (developed around the Web 2.0 portals), the ease of access to OERs, the informal learning opportunities which characterize the learners' autonomy in identifying their own patterns to knowledge, thus changing the way learning occurs. The huge amount of data produced during a MOOC suggest that Learning Analytics techniques could be extremely useful to support the learning and teaching processes during the course. Nevertheless, there is not yet a substantial body of literature on the Learning Analytics of MOOCs (Clow, 2013). D'Aquin in (d'Aquin, 2012) has pointed out how the Linked Data can be exploited in the educational field. In fact, LD offers educational benefits for teaching and learning by providing integration mechanisms of heterogeneous resources from diverse repositories and sources and by supporting automatic search techniques that provide learners with educational resources semantically related to a specific knowledge field. As a consequence, the huge amount of datasets of LD can be exploited for educational purposes, thus becoming an alternative source of Open Educational resources. The recent evolution of LD and its datasets has had a remarkable influence on Learning Analytics as well (Gasevic, 2012), so that Learning Analytics is not limited to numeric data, but it has to take into account data and their semantic representation. The aim of this paper is therefore to highlight - through the analysis of the recent literature - how Learning Analytics can enhance learning experiences in Open Education settings based on MOOCs and LD.