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PurposeThis paper aims to develop indicators of happiness in learning of the Thai open university (TOU)'s undergraduate students.Design/methodology/approachSampling for the study was comprised of two groups. Group I comprised eight lecturers who are experts in their disciplines and six students who were purposively sampled. The focus group was used to validate the appropriateness of the indicators. In Group II, 332 students were engaged in a multistage sampling process. The responses were analyzed using descriptive statistics, coefficient correlation, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).FindingsThe indicators of happiness in learning of undergraduate students of TOU were classified in six categories. These included satisfaction with learning environment (five indicators), learning anxiety (five indicators), satisfaction with learning (five indicators), enthusiasm to learn (six indicators), self-satisfaction (six indicators) and readiness to learn (seven indicators). The six categories explained happiness in learning of undergraduate students of TOU at the 65% and fit empirical data.Practical implicationsThe TOU can use the indicators for the assessment of happiness in learning of its students as well as guidelines for the improvement of its student learning environments.Originality/valueThere have been very few studies on indicators of happiness in learning of TOU students. Most were done at the basic education level. This study disclosed the six factors affecting happiness in learning of TOU students; therefore, it should inspire and draw attention of many in the field of higher education distance learning.

Multimodal learning analytics (MMLA), which has become increasingly popular, can help provide an accurate understanding of learning processes. However, it is still unclear how multimodal data is integrated into MMLA. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper systematically surveys 346 articles on MMLA published during the past three years. For this purpose, we first present a conceptual model for reviewing these articles from three dimensions: data types, learning indicators, and data fusion. Based on this model, we then answer the following questions: 1. What types of data and learning indicators are used in MMLA, together with their relationships; and 2. What are the classifications of the data fusion methods in MMLA. Finally, we point out the key stages in data fusion and the future research direction in MMLA. Our main findings from this review are (a) The data in MMLA are classified into digital data, physical data, physiological data, psychometric data, and environment data; (b) The learning indicators are behavior, cognition, emotion, collaboration, and engagement; (c) The relationships between multimodal data and learning indicators are one-to-one, one-to-any, and many-to-one. The complex relationships between multimodal data and learning indicators are the key for data fusion; (d) The main data fusion methods in MMLA are many-to-one, many-to-many and multiple validations among multimodal data; and (e) Multimodal data fusion can be characterized by the multimodality of data, multi-dimension of indicators, and diversity of methods.

The purpose of this study is to describe the ability of students to solve relation and function problems in based on the learning indicator perspective. This type of research is a qualitative descriptive study. This research was conducted at two Madrasah Tsanawiyah (MTs) schools in West Lombok with 79 students. This research instrument includes a diagnostic test sheet and interview guidelines. The procedure of this study includes the provision of diagnostic test sheets to obtain the level of student ability. Furthermore, with a purposive sampling technique interview samples were set. Based on the results of the study obtained information that the ability of students to solve the problem of relations and functions are in the low category. Based on the learning indicators obtained the following findings: (1) the ability of students to explain the definition of relations and functions are in the low category; (2) the ability of students to determine examples and not examples of functions is in the medium category; (3) students' ability to determine the domain, codomain, and range of a function is in the medium category; (4) students' ability to draw Arrow and Cartesian Diagrams are in the medium category; (5) students' ability to determine the value of a function if the value of the variable is known to be in the low category; (6) students' ability to determine the value of a variable if the value of the function is known to be in the very low category; and (7) the ability of students to make function formulas is in the very low category

The UNESCO Institute for Statistics (UIS) has been measuring ICT in education since 2009, but with such rapid change in technology and its use in education, it is important now to revise the collection mechanisms to focus on how technology is being used to enhance learning and teaching. Sustainable development goal (SDG) 4, for example, moves beyond measures of access and increasingly focuses on the sustainability of education including issues of educational quality and student outcomes. A reassessment of how ICT in education is measured to support the attainment of the SDGs by 2030 is thus a timely endeavour. The paper discusses four aspects: (1) evolving mission, methods and core principles of ICT in learning and teaching; (2) nature of ICT in education in accelerating the emergence of new learner-centred pedagogies; (3) types of learning activities associated with the use of ICT including those for leaders, teachers and students; and (4) usage and deployment patterns. This paper proposes extensions and adaptions to the existing UIS data collection instrument to enrich its capacity for understanding how ICT is being used in learning and teaching.

Background Understanding how clinical learning takes place and what could stand as an indicator of clinical learning is crucial. There are existing challenges in the clinical learning environment that require clinical indicators. These serve as accountability standards in settings that have challenges of human resources and material poverty. Thus, clinical indicators are pre-requisites for self-regulation and self-directedness to promote lifelong learning. The reality that exists in today’s Malawian health education institutions and clinical settings requires that those in training receive support and guidance on how essential competencies and skills can be acquired during training. Objectives The objective of this scoping review was to identify current literature on clinical learning indicators among health professional students. Method The Joanna Briggs Institute’s (May 2020) standards for scoping reviews including narrative synthesis were followed in the conduct of this review. The protocol was registered in the Open Science Framework https://osf.io/yj9nr. Results The results generated seven themes on clinical learning process and these are (1) planning for learning, (2) awareness of self-directedness in clinical learning, (3) knowledge of achievement of learning outcomes, (4) educators’ evidence of students’ clinical learning, (5) students’ perspective on clinical learning, (6) students’ knowledge of achievement in practice and (7) impact of prior knowledge on clinical learning. Conclusion Clinical learning indicators among undergraduate health professionals are essential and clinical learning should be a planned endeavour by the students before the clinical placement settings. Contribution This study contributed to understanding clinical learning indicators and self-regulated learning practices among healthcare students.

Educational institutions are transferring analytics computing to the cloud to reduce costs. Any data transfer and storage outside institutions involve serious privacy concerns, such as student identity exposure, rising untrusted and unnecessary third-party actors, data misuse, and data leakage. Institutions that adopt a “local first” approach instead of a “cloud computing first” approach can minimize these problems. The work aims to foster the use of local analytics computing by offering adequate nonexistent tools. Results are useful for any educational role, even investigators, to conduct data analysis locally. The novelty results are twofold: an open-source JavaScript library to analyze locally any educational log schema from any LMS; a front-end to analyze Moodle logs as proof of work of the library with different educational metrics and indicator visualizations. Nielsen heuristics user experience is executed to reduce possible users’ data literacy barrier. Visualizations are validated by surveying teachers with Likert and open-ended questions, which consider them to be of interest, but more different data sources can be added to improve indicators. The work reinforces that local educational data analysis is feasible, opens up new ways of analyzing data without data transfer to third parties while generating debate around the “local technologies first” approach adoption.

Although learning indicators are now properly studied and published, it is still very difficult to manage them freely within most distance learning platforms. As all activity indicators need to collect and analyze properly traces of the learning activity, we propose to use these traces as a starting point for a platform independent Trace Based-Indicator Management System (TB-IMS). This approach allows learning indicators to be created and reused in such a way that there is no need to modify the computer code of the learning platform. This paper presents the underlying theory and how this theory is implemented in a first TB-IMS. This TB-IMS is illustrated through an actual learning situation based on a Moodle platform. This approach is compared with similar attempts to manage learning indicators properly and is available for use with any other learning platform, provided the TB-IMS can access the learning platform traces.

This article deeply explores the behavior and effect of online learning in ideological and political education in colleges and universities, firstly, it clarifies the mechanism of the occurrence of online ideological and political learning behavior, and constructs the corresponding indicators of learning behavior. Using CART tree and XGBoost model, the article ranks the feature importance of learning behaviors. It combines with Bayesian network to construct a comprehensive analysis model to explore the causal relationship between learning behaviors and learning effects. By analyzing the data of M online learning platform in 2021, the study found that resource learning features have the most significant impact on learning performance, especially the indicators of video viewing time, number of homework submissions and number of online discussions. The study results show that when learning resources are rich and professional, learning performance is significantly improved, providing an effective way to optimize the teaching quality of online Civics education.

Accreditation of Prior Experiential Learning (APEL) is an accreditation system involving assessment of a person's recognition of experience through formal, informal and non-formal learning. Generally, APEL pro-vides opportunity for individuals to gain access to higher learning institu-tions and gain credits in learning based on experience gained through equivalence checking. Therefore, this study aimed at obtaining feed-back/input on items for nine (9) content learning indicator domains that will be used for the determination of learning content in assessing the equivalence checking between the skills module and the academic module for the APEL process. The nine (9) domains are knowledge (current) in the field; practical skills; social skills & accountability; values, attitudes & professionalism; communication skills, leadership & teamwork; problem solving skills & scientific skills; management skills, entrepreneurship & innovation; information management & lifelong learning; and interdisci-plines. This study uses a quantitative approach which uses questionnaire to get feedback from respondents. A total of 32 respondents comprising lec-turers from seven (7) faculties of Universiti Tun Hussein Onn Malaysia. The data are analysed using Statistical Packages for Social Science 23.0 (SPSS 23.0). The finding of this study found that all the items in the Con-tent Learning Indicator in equivalence checking between skills module and academic module of the APEL process are very appropriate. Improvements are made such as feedback and recommendations provided by respondents.

Blended teaching combines the advantages of online teaching and offline teaching, which breaks the limitation of time and space to a certain extent and is of great significance to helping learners improve their learning efficiency and teachers’ teaching level and quality. This paper firstly designs a blended teaching model for the background of Internet+ education and improves the functional design of each part of the model. Secondly, the extreme gradient prediction model of students’ academic level is proposed for the blended model, and a strong learner is formed by integrating multiple weak learners to correct the misclassification. Finally, the blended model of instruction is evaluated and analyzed. The model achieves an accurate prediction accuracy of 65% for MEDIUM students and 75% for PERFECT students, with an average prediction accuracy of 58.4%, which is more than 50%, and can predict students’ academic level situations more accurately. The functional indicators all scored 74.4, the efficiency indicators all scored 71.35, and the usability of the platform exceeded 70. The Internet+education platform’s professional-type English, the online and offline hybrid teaching model, can effectively improve learners’ learning efficiency, as well as improve teachers’ teaching standards and quality.