Advances in information and communication technologies have contributed to the increasing use of virtual learning environments as support tools in teaching and learning processes. Virtual platforms generate a large volume of educational data, and the analysis of this data allows useful information discoveries to improve learning and assist institutions in reducing disqualifications and dropouts in distance education courses. This article presents the results of a systematic mapping study aiming to identify how educational data mining, learning analytics, and collaborative groups have been applied in distance education environments. Articles were searched from 2010 to June 2020, initially resulting in 55,832 works. The selection of 51 articles for complete reading in order to answer the research questions considered a group of inclusion and exclusion criteria. Main results indicated that 53% of articles (27/51) offered intelligent services in the field of distance education, 47% (24/51) applied methods and analysis techniques in distance education environments, 21% (11/51) applied methods and analysis techniques focused on virtual learning environments logs, and 5% (3/51) presented intelligent collaborative services for identification and creation of groups. This article also identified research interest clusters with highlights for the terms recommendation systems, data analysis, e-learning, educational data mining, e-learning platform and learning management system.
The article presents results of the empirical research revealing readiness of adults to participate in the lifelong learning process using e-learning, m-learning and t-learning technologies. The research has been carried out in the framework of the international project eBig3 aiming at development a new distance learning platform blending virtual learning environments, television and mobile technologies. Readiness to learn in a distance mode using e-learning, m-learning and t-learning technologies has been analysed on the ground of self-assessment of adults' computer literacy, usage of e-services including e-learning in a distance mode, experience in and attitude towards the choice of the mode of learning.
In this paper, we propose the development of a web-based, intelligent instruction system to help elementary school students for mathematical computation. We concentrate on the intelligence facilities which support diagnosis and advice. The existing web-based instruction systems merely give information on whether the learners' replies are `correct' or `incorrect', and only offer evaluations of the learners' results in terms of points. What is needed is a web-based instruction system that diagnoses the learner's comprehension status, and provides cause: why did the learner make the error? Our system has a facility that analyses the learner's weak points and has the ability to diagnose the cause of the error, giving advice to the learners and more detailed error information than extant systems. By accumulating user behavior and analyzing the learner's responses, our system provides relevant, individualized information, along with advice for the learners.
While researchers working within the Student Learning Research framework have developed or adapted questionnaires to gather information on students' experiences of blended learning, no questionnaire has been developed to enquire about teachers' experiences in such learning environments. The present article reports the development and testing of a novel questionnaire on `approaches to e-teaching', which may be employed to investigate the experience of teaching when e-learning is involved. Results showed suitable reliability and validity. Also, when exploring associations between the novel questionnaire scales and those of the well-known `approaches to teaching' inventory (Prosser and Trigwell, 2006), results from correlation and cluster analyses suggest that student-focused approaches to teaching are needed for significant use of digital technology to emerge. For practice, this relevant outcome implies that teaching needs to be considered holistically when supporting teachers to incorporate e-learning in their practice: because it seems they approach online teaching coherently with the face-to-face side of the blended experience.
E-learning students are generally heterogeneous and have different capabilities knowledge base and needs. The aim of the Sumy State University (SSU) e-learning system project is to cater to these individual needs by assembling individual learning path. This paper shows current situation with e-learning in Ukraine, state-of-art of development of the adaptive e-learning systems and shows results of SSU research in this area. Nowadays the received solutions are different from the known analogues considering an expanded set of information about the features of a particular student's learning activities (19 indicators are analysed, including indicators of progress such as the level of knowledge and student individual features). The corresponding software solutions are being tested in the SSU e-learning environment.
Considering learning and how to improve students' performances, an adaptive educational system must know how an individual learns best. In this context, this work presents an innovative approach for student modeling through probabilistic learning styles combination. Experiments have shown that our approach is able to automatically detect and precisely adjust students' learning styles, based on the non-deterministic and non-stationary aspects of learning styles. Because of the probabilistic and dynamic aspects enclosed in automatic detection of learning styles, our approach gradually and constantly adjusts the student model, taking into account students' performances, obtaining a fine-tuned student model. Promising results were obtained from experiments, and some of them are discussed in this paper.
The Internet has recently encouraged the society to convert almost all its needs to electronic resources such as e-libraries, e-cultures, e-entertainment as well as e-learning, which has become a radical idea to increase the effectiveness of learning services in most schools, colleges and universities. E-learning can not be completely featured and met without e-testing. However, in many cases e-testing tools are suitable just for traditional/theoretical knowledge testing, covered by such items as questions, quizzes, matching boxes and other. The article ``A Method for Automated Program Code Testing'' tackles the lack of functions in e-testing systems and suggests e-assessment possibilities for students who study computer science, especially programming. The article analyzes the method that allows freely entering answers to questions, checking program syntax during the testing and enables automatic written code checking and evaluation.
Blended learning is becoming an attractive model in higher education as new innovative information technologies are becoming increasingly available. However, just blending face-to-face learning with information technologies cannot provide effective teaching and efficient solutions for learning. To be successful, blended learning must rely on solid learning theory and pedagogical strategies. In addition, there is a need for a design-based research approach to explore blending learning through successive cycles of experimentations, where the shortcomings of each cycle are identified, redesigned, and reevaluated. This paper reports on a study conducted on a blended learning model in Java programming at the introductory level. It presents the design, implementation, and evaluation of the model and its implications for the learning of introductory computer programming.
In this paper, we present an open-source program visualization tool, Jeliot 3. We discuss the design principles and philosophy that gave rise to this successful e-learning tool and to several other related environments. Beside Jeliot 3, we introduce three different environments, BlueJ, EJE, and JeCo that use Jeliot 3 as a plug-in to allow visualization of the program code. Another system, FADA, is a tool that was derived from Jeliot 3 but serves for different pedagogical goals. A community of users and developers of these projects has been created and supported, that allows for global and iterative improvements of the Jeliot 3 tool. This way, both academic research and feedback from the user community contribute to the development. We compare the presented approach of the tool development to some of the current tools and we discuss several instances evidencing a particular success.