The Development of Web-Based Learning Models as A Learning Medium for Students of Audio Video Electronics Competencies

At present distance education has reached almost the entire archipelago. These developments occur a lot in higher education. This benefit is also brought about in secondary education, as seen in SMKN 1 Jetis, especially in the electronics field. Web media with URL ruangpakdedy.com was developed to answer the many electronic learning materials that exist in cyberspace but are still scattered, so the web is made to collect various sources of electronic material in one site. This ease is expected to increase skills that have a greater portion than cognitive knowledge in vocational education. The development used refers to the 4D Thiagarajan model by going through the planning process, material testing by material experts, and media testing by media experts, revisions, products tested on students with good results. As a complement on the web also added assignments that are connected to the LMS page and quiz.

An open learning environment for the diagnosis, assistance and evaluation of students based on artificial intelligence

The personalized diagnosis, assistance and evaluation of students in open learning environments can be a challenging task, especially in cases that the processes need to be taking place in real-time, classroom conditions. This paper describes the design of an open learning environment under development, designed to monitor the comprehension of students, assess their prior knowledge, build individual learner profiles, provide personalized assistance and, finally, evaluate their performance by using artificial intelligence. A trial test has been performed, with the participation of 20 students, which displayed promising results

Blended Learning Station-Rotation Model: Does it Impact on Preservice Teachers' Scientific Literacy?

This study aims to test the effectiveness of the blended learning rotation model to improve the scientific literacy skill of prospective teacher students. The design of this study used the Posttest-only design with nonequivalent groups. The research subjects were 76 students majoring in biology education at Universitas Mataram. The research was done to two experimental groups, namely the blended learning station rotation model (BLSRM) and regular learning (RL). The research instrument used a multiple-choice test which refers to the aspects of scientific literacy competence according to PISA. The data analysis technique was carried out descriptively, complete with the Hotelling's T2 test to test the effect of BLSRM on students' scientific literacy skills. The results showed that students' scientific literacy skills were in the very low category (mean BLSRM = 38.50, RL = 34.22) and there was no effect of BLSRM on students' scientific literacy skills (Hotteling Trace = .098 and Sig. = .080). To improve students' scientific literacy skills, BLSRM needs to be combined with other models such as problem-solving model, so it can be an effective strategy in science learnin

A case study for measuring informal learning in PLEs

The technological support for learning and teaching processes is constantly changing. Information and Communication Technologies (ICT) applied to education, cause changes that affect the way in which people learn. This application introduces new software systems and solutions to carry out teaching and learning activities. Connected to ICT application, the emergence of Web 2.0 and its use in learning contexts enables an online implementation of the student-centred learning paradigm. In addition, 2.0 trends provide “new” ways to exchange, making easier for informal learning to become patent. Given this context, open and user-centered learning environments are needed to integrate such kinds of tools and trends and are commonly described as Personal Learning Environments. Such environments coexist with the institutional learning management systems and they should interact and exchange information between them. This interaction would allow the assessment of what happens in the personal environment from the institutional side. This article describes a solution to make the interoperability possible between these systems. It is based on a set of interoperability scenarios and some components and communication channels. In order to test the solution it is implemented as a proof of concept and the scenarios are validated through several pilot experiences. In this article one of such scenarios and its evaluation experiment is described to conclude that functionalities from the institutional environments and the personal ones can be combined and it is possible to assess what happens in the activities based on them.Peer ReviewedPostprint (published version

AI-Based Collaborative Teaching: Strategies and Analysis in Visual Communication Design

With the rapid development of technology, AI has been widely applied in multiple fields, especially the field of education. As a discipline involving art, technology and creativity, visual communication design is facing the challenge of keeping up with the times and combining new technologies for innovation. Collaborative teaching model emphasizes multi-party participation and collaborative learning, and its proposal has injected new vitality into traditional educational patterns. However, existing studies, which combine collaborative teaching model with artificial intelligence, still have limitations in application and practice, and most of them remain in the theoretical discussion stage and lack empirical support. This study aimed to make up for this deficiency. After in-depth analysis of educational data, a forecasting model of collaborative teaching demand based on AI was proposed. Course content suitable for the collaborative teaching model was further planned for the education in visual communication design

Distributed Communicative Language Training Platform Using Automatic Speech Recognition Technology for Smart University

The purpose of this research is to achieve the following objectives: 1) Synthesize documents and international research on the characteristics of a smart university. 2) Synthesize the processes of distributed communicative language training (DCLT). 3) Design the system architecture of a DCLT platform that utilizes automatic speech recognition (ASR) technology for a smart university. 4) Evaluate the appropriateness of a DCLT platform that utilizes ASR technology for a smart university. Nine experts were selected for this research. They were required to have more than five years of relevant experience in the field, including expertise in system architecture, distributed enterprise, language teaching, and ASR. The research instruments included a suitable assessment form for evaluating the system architecture of a DCLT platform that utilizes ASR technology for a smart university. The results of this research indicate that the DCLT platform, which utilizes ASR technology, was considered suitable for a smart university

Selecting the Best K Features for Predicting Student Participation in Generic Competency Development Activities in Higher Education

Generic competency (GC) is an essential but often overlooked aspect of developing students in higher education. While there is much research about using technologies to develop discipline- specific skills for students, the use of technologies in GC development is insufficient. In particular, more research is needed on using technologies to predict student participation in GC development activities (GCDAs). Machine learning (ML) can use student characteristics, known as features, to predict their involvement in GCDAs. However, too many features will slow down the prediction process and reduce the ability to pinpoint the best features for prediction. This study explored an effective way to identify the minimal number of features essential for predicting student participation in GCDAs. The findings help educators develop recommendation systems to help students select the most beneficial GCDA for their holistic development. We collected 98 features from 9570 students from a community college. Then, we applied the Principal Component Analysis and SelectKBest algorithms to reduce the number of features from 98 to 8. Finally, we compared the accuracy of predictions using KNN and ANN based on the all-feature dataset with those based on the reduced-feature dataset. The results showed that the reduced-feature dataset maintained good prediction accuracy and enabled the educator to recommend the GCDAs to students. The findings could drive further research and development in applying machine learning technologies to enhance the recommendations for GCDAs for higher-education students

Analysis of Abstractive and Extractive Summarization Methods

This paper explains the existing approaches employed for (automatic) text summarization. The summarizing method is part of the natural language processing (NLP) field and is applied to the source document to produce a compact version that preserves its aggregate meaning and key concepts. On a broader scale, approaches for text-based summarization are categorized into two groups: abstractive and extractive. In abstractive summarization, the main contents of the input text are paraphrased, possibly using vocabulary that is not present in the source document, while in extractive summarization, the output summary is a subset of the input text and is generated by using the sentence ranking technique. In this paper, the main ideas behind the existing methods used for abstractive and extractive summarization are discussed broadly. A comparative study of these methods is also highlighted

Improving Academic Decision-Making through Course Evaluation Technology

The objective of this study is to offer a broad understanding of how end of course evaluations can be used to improve the academic outcomes of a higher education institute. This paper presents the key findings from a study conducted using twenty-three academic degree-programs, regarding their use of end of course evaluation technology. Data was collected from an online survey instrument, in-depth interviews with academic administrators, and two case studies, one in the US and another in the UAE. The study reveals that while historically end of course evaluations were primarily used to gauge the performance of instructors in the classroom, there are several new trends in the use of end of course evaluations that can help higher education institutions improve academic assessment, teaching and learning, and academic administration decision making. Those trends include sectioning and categorization; questions standardization and benchmarking; alignment with key performance indicators and key learning outcomes; and grouping by course, program outcome, program, college, etc. in addition to those vertical structures, higher education institutions are vertically examining a specific question(s) across. End of course evaluations are now poised as an integral tool and a key health indicators of academic programs

Big Data Emerging Technology: Insights into Innovative Environment for Online Learning Resources

Digital devices like tablets, smart phones, and laptop have become increasingly raised and utilised in higher education. As a result, current trends on ICT (information and communication technology) used in education begin widely with focusing on teaching and learning. The new concept of big data in recent ICT domain extends the promising research direction on online learning and big data integration through promising content that can be tailored for each student based on the context and Internet behaviour of users in online learning. This paper aims to explore innovative design for innovative online learning in Higher education using Big Data approach. Critical review from referred journals and books was conducted using thematic analysis. This paper proposes model reference which can be implemented with the technology in teaching and learning to improve student learning environment and outcomes and to enhance students’ development, performance and achievement in learning process in higher education