Learn Smarter, Not Harder – Exploring the Development of Learning Analytics Use Cases to Create Tailor-Made Online Learning Experiences

Our world is significantly shaped by digitalization, fostering new opportunities for technology-mediated learning. Therefore, massive amounts of knowledge become available online. However, concurrently these formats entail less interaction and guidance from lecturers. Thus, learners need to be supported by intelligent learning tools that provide suitable knowledge in a tailored way. In this context, the use of learning analytics in its multifaceted forms is essential. Existing literature shows a proliferation of learning analytics use cases without a systematic structure. Based on a structured literature review of 42 papers we organized existing literature contributions systematically and derived four use cases: learning dashboards, individualized content, tutoring systems, and adaptable learning process based on personality. Our use cases will serve as a basis for a targeted scientific discourse and are valuable orientation for the development of future learning analytics use cases to give rise to the new form of Learning Experience Platforms

Learning Motivation increased due to a Relaxed Assessment in a Competitivee-Learning Environment

In this work, e-learning is used to increase learners 'motivation and competence in addition to learning complementary environment. This work is based on of the 7-years of hybrid e-learning classes on Operating System, Computer Network, Network Security, Network Management subjects at 3 Indonesian universities, namely, Surya University, STKIP Surya, and IBI Darmajaya. Most of STKIP Surya's students are from Papua and need more attention and motivation than others. Relaxed assessment processes are performed on each module in addition to mid term and final exams. On average, there are 12-18 modules in each subject matter. To motivate the learners, they may perform as many exams as ones' wish to attained the highest possible mark within the semester on all exams and quizzes. Relaxed assessment processes to attained maximum grades seems to increase the learners' motivation as some learners' retrying in exceeding 50 times for the 100 questions final shown in the Level of Competency (LoC) measurement. In addition, to anticipate, any cheating, eliminate remedial and cost savings, all exams are done on moodle via web. Such method of a whole semester relaxed assessment equipped with about 2000-6000 questions bank per subject and is for the first time performed in Indonesia. This study is also found an increase in the majority of learner's understanding on the subject is very good without remedial. Thus, e-learning seems not only complement learning processes but also able to motivate and to improve the competence of learners as well creating healthy competitive environment among learners

A Recommender System for Adaptive Examination Preparation using Pearson Correlation Collaborative Filtering

Distance learning is any type of far-off instruction where the understudy isn't actually present for the exercise. It is blasting gratitude to the force of the Internet. Distance learning plays a vital role for examination preparation where multiple choice questions can be utilized to evaluate the performance of students. Multiple Choice Question (MCQ) is a type of question used in the examination to evaluate the performance of students accordingly where usually four options are given along with the question, and one has to choose the correct answer. This research includes a simulation model that has been built to keep the learners continue to learn the subjects they might be weak in. We have developed a methodology that may guide a student to update his/her area of weakness by using a recommender system based on Pearson Correlation Collaborative Filtering approach. The paper describes a recommender system that will keep track of a learner's profile and create an adaptive training mechanism using the performance matrix

The role of error factors in teaching

This thesis describes a wide-ranging enquiry into the nature, identification and treatment of pupil error. It takes, as its main point of departure, the work of Harlow on the role of 'error factors' in learning and it was undertaken in the belief that educators could considerably enhance the effectiveness of their teaching by making more insightful appraisals of the learning obstacles, (or error factors) of their pupils. Harlow first introduced the concept of error factors following his research into 'learning set' formation. Lewis and Pask subsequently incorporated Harlow's ideas in their own work, 'and laid particular stress on the significance of error factors which give rise to whole classes of errors. The concept of error factors and the role of error factors in teaching, is examined in this thesis in both psychological and philosophical terms, because the two approaches are mutually illuminating. Despite scant literature in this sphere the entire topic is opened up to systematic examination. Hypotheses are presented concerning the role and nature of error factors, and novel strategies are proposed for treating them. A special diagnostic framework for error factors has been formulated, an. 1 its effectiveness investigated in a series of Case Studies and pilot experiments, culminating in a major experimental investigation concerning the overall role of error factors in teaching. Strategies for error factor prediction and prevention are presented and examined, including the use of algorithms. Major consideration is given to the exploitation of error factors as powerful tools to enhance learning. There is a detailed theoretical discussion at each stage, so that the full significance of the findings can be assessed. Inferences concerning the role of error factors in teaching are examined in conjunction with methodological and other implications arising from the experimental findings. A number of weaknesses of current teaching strategies are identified, and the thesis concludes with various speculations (e. g. to do with the use of computer assisted instruction in this context) which have been prompted by this investigation

AI-Based Adaptive Learning: A Systematic Mapping of the Literature

With the aid of technology advancement, the field of education has seen a noticeable transformation. The teaching-learning process is now more interactive and is no longer restricted to students' physical presence in the classroom but instead makes use of specialized online platforms. In recent years, solutions that offer learning routes customized to learners' needs have become more necessary. In this regard, artificial intelligence has served as an excellent answer, allowing for the building of educational systems that can accommodate a wide range of student needs. Through this paper, a systematic mapping of the literature on AI-based adaptive learning is presented. The examination of 93 articles published between 2000 and 2022 made it possible to draw several conclusions, including the number of adaptive learning environments based on AI, the types of AI algorithms used, the objectives targeted by these systems as well as factors related to adaptation. This study may serve as a springboard for further investigation into how to address the problems raised by the current state.&nbsp

Design of an E-learning system using semantic information and cloud computing technologies

Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process. We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers. In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy. Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí