Although university students use their digital devices for almost everything, current studies shows that students have difficulties with digital learning because they lack in self-regulated skills which in return lead to low performance. Self-regulated learning strategies (SRLS) are used assist students to learn efficiently. While many researchers have investigated SRLS towards academic outcomes such as grades, little is known about the use of SRLS towards non-academic outcomes that are also essential to assist university students’ learning progression. Hence, there is a need to understand how best to utilise SRLS to drive positive non-academic outcomes in digital learning within a blended learning environment. The systematic review methodology follows PRISMA guidelines to explore the current literature. Different sources were searched using predefined search items. A total of 239 retrievals were found which were screened for duplication. A closer screening was done on the abstracts and titles of 239 papers after duplication removal. 28 full text papers were evaluated for eligibility. Finally, 14 papers were then selected for the review. Most of the papers included in the review were peer-reviewed articles published in social science and educational journals. List of self-regulated learning strategies and non-academic outcomes used in a blended learning environment in higher education institutions were identified. Majority of the 14 reviewed papers investigated metacognitive knowledge, resource management and motivational belief strategies towards learning performance whereas cognitive engagement strategies was the least researched. Results revealed that generally, SRLS positively correlate with non-academic outcomes. At the end of the review, research gap and the future direction are presented.

Self-regulated learning strategies and non-academic outcomes in higher education blended learning environments: A one decade review

Corona Virus Diseases (Covid-19) has become an epidemic in Indonesia and even has become a global pandemic in 2020. Various impacts of Covid-19 is one of them changes learning strategy. One of them is the change from conventional learning to internet-based learning or electronic learning ( e-learning ). The purpose of this study was to describe the e-learning in natural science and environmental learning during the Covid-19 outbreak. The research method used descriptive method and data collection techniques using a survey through Google Form. The study was conducted during March 2020 in Bekasi city and Bandung city, when the Covid-19 outbreak began in Indonesia. The results of this study indicate that there are many limitations. Findings showed that Whatsapp was the highest e-learning media (65.54%). Related to the biggest obstacle is about internet quota in a cellular phone (27.03%). Then students think the video (35.14%) was the best type of file to be shared in e-learning . Then the majority of students (54.73%) thought that the most effective duration for e-learning was 2-3 hours. The results of this study indicate that e-learning was not yet perfect and needs to be improved. The conclusion of this study was e-learning needs to be improved and need innovation.

/pdf/covid-19-dan-e-learning-perubahan-strategi-pembelajaran-15gbolbamv.pdf

COVID-19 Dan E-Learning: Perubahan Strategi Pembelajaran Sains Dan Lingkungan Di Smp

Identifying emotions experienced by students in a learning environment contributes to measuring the impact when technologies such as augmented reality (AR) are implemented in the educational field. The most frequent methods for collecting emotional metrics are questionnaires, surveys, and observations, but each of these processes lacks objectivity and veracity. For this reason, this study proposes, develops and tests a learning analytics scheme, based on the density-based spatial clustering of applications with noise algorithm, as a clustering technique with time series analysis, using the brain- computer interface device, Emotiv EPOC, as a way to collect emotional metrics. The above, in order to perform emotional behavior characterization by using AR in a learning environment through AR-Sandbox. The proposed method shows a clear inclination in the tendency of each emotion in each cluster, allowing classification of children during their interaction with the immersive environment, as well as the ability to distinguish each group of students.

Emotional characterization of children through a learning environment using learning analytics and AR-Sandbox

The E-learning systems are important for the universities due to the gained benefits from these systems. By using the E-learning system, the learning expenses can be reduced, the learning management would be improved, and the learning quality could be enhanced. Like any technology system, the students' motivation and positive attitude are success keys of the E-learning adoption by the universities. There are many difficulties faced by the students in using the E-learning system such as the technical difficulties, services difficulties, awareness, interactivity, and security concerns. This study aims to study the effects of the E-learning difficulties on the attitudes of using the E-learning systems through the path of students' motivation. The Technology Acceptance Model (TAM) is adopted to construct the conceptual model in this study. This study considered the E-learning difficulties as independent factor, the mediating factor is the motivation of using the E-learning systems, and the dependent factor is the attitudes toward the using of the E-learning systems. Based on the constructed model, three research hypotheses are developed to test the relationship between the E-learning difficulties and motivation, between motivation and attitudes, and between difficulties and attitudes through the motivation. The quantitative data method using questionnaire is used to collect the study data. The data collected from 154 students from Ajloun University as a case study of Jordanian universities. The case study is selected due the experience of the students in using E-learning system called BigBlueButton. The questionnaire data were analyzed using AMOS tool in order to discover the direct and indirect paths between the study variables. The significant results show that there moderator effects of the students motivation on the relationship between the E-learning difficulties and the attitudes toward the using of these systems. The P-values is significant at p < 0.05 on the relationship between the study variables. Another important result is that the difficulties of E-learning such as technical, services, and interactivity should be avoided to improve the motivation level of using the E-learning systems. Also, the low motivation level lead to decrease the attitudes level toward the using of E-learning systems. The contribution of this study represented by fill the research gap of the motivation path on the relationship between the E-learning difficulties and attitudes. This study gives the university important details about the features of success implementations of E-learning systems.

/pdf/motivation-path-between-the-difficulties-and-attitudes-of-nmcsooyhz1.pdf

Motivation Path between the Difficulties and Attitudes of Using the E-Learning Systems in the Jordanian Universities: Aajloun University as a Case Study

E-learning has been extensively implemented in universities and motivation is one of the important factors contributing the successful learning. However, few studies focus on the relationship between student motivation level and academic performance in e-learning. Therefore, we explored the relationship between motivation and academic achievement among students in Thai universities. 115 social science students filled in an instructional materials motivation survey and the data was analyzed by using SPSS software. The majority of students were found to have upper to medium motivation levels in e-learning. Further, there was a weak, positive correlation between motivation level and academic achievement, but it was not statistically significant. More results are discussed in this paper.

http://www.ijiet.org/vol10/1360-OC3024.pdf

The relationship between academic performance and motivation level in e-learning among Thailand university students

The rapid growth of Internet technology has resulted in the development of e-learning in higher education institution with the purpose of enhancing students’ performance in their learning practice. In this regard, motivation is one of the important predictors of intention to use e-learning that can in turn make impact on students’ learning practice. By knowing the motivation level of students, instructors can provide suitable teaching materials in e-learning for students to make them more engaged in e-learning activities. Nonetheless, based on the recent review studies result, there is limited research on investigating motivation level of students in e-learning in higher education institution in Thailand. Hence, this study aims to explore the motivation level of students in e-learning in higher education institutions in Thailand, using the Instructional Materials Motivation Survey and interview as the research instruments. A total of 174 students from higher education institutions in Thailand were involved in this study. SPSS software was used to analyse the data to investigate the motivation level of students, and also to compare the different motivation level of students between gender, degree level of study, branch of study and year of study. The result shows that most of the students had upper medium level of motivation level in e-learning, and there was a statistically significant difference in motivation level between the different degree levels of students. Also, the ways to enhance motivation suggested by the respondents were also discussed in this paper as a reference for the future use.

Examining the motivation level of students in e-learning in higher education institution in Thailand: A case study

The application of learning analytics in an online learning environment is increasing among researchers in educational fields because it can assist in providing standard and measurable decision making about student success. In this regard, there is a need for the online learning society and practitioners to be informed about how learning analytics is applied in the online learning environment. Therefore, this systematic review article aims to offer the reader essential details regarding the practical usage of learning analytics techniques in online learning environments to improve the quality of teaching and learning practices. The focal point of this review is threefold: to ascertain the focus of learning analytics research in online learning environments and its significant results, to identify the types of student-related analytics data in online learning environments and the issues related to analytics data, and to inform about the extent of interventions that have been applied in the learning analytics context, if any. Four procedures suggested by PRISMA were applied when conducting a systematic review. A total of 34 articles were chosen according to the review selection guideline by searching through online databases, including ACM Digital Library, LearnTechLib, ERIC, International Forum of Educational Technology & Society, ScienceDirect, Web of Science, Scopus, Society for Learning Analytics Research, and SpringerLink. The search keywords used were learning analytics data and online learning, learning analytics data and learning analytics intervention. The results were analysed based on the three focuses mentioned earlier and were interpreted accordingly. It is found that the applied focus of learning analytics is more oriented towards monitoring/analysis and prediction/intervention, and the commonly used types of student-analytics data are learning behaviour data and learning level data. Additionally, the intervention is still in the process of being developed. Lastly, the directions for future studies and limitations are also provided so as to further develop this emerging research area, and the findings can be a good reference point for other researchers.

Learning Analytics in Online Learning Environment: A Systematic Review on the Focuses and the Types of Student-Related Analytics Data

Developing a Learning Analytics Intervention in E-learning to Enhance Students’ Learning Performance: A Case Study

Modern text-to-speech voices can convey social cues ideal for narrating multimedia learning materials. Amazon Alexa has a unique feature among modern text-to-speech vocalizers as she can infuse enthusiasm cues into her synthetic voice. In this first study examining modern text-to-speech voice enthusiasm effects in a multimedia learning environment, a between-subjects online experiment was conducted where learners from a large Asian university (n = 244) listened to either Alexa’s: (1) neutral voice, (2) low-enthusiastic voice, (3) medium-enthusiastic voice, or (4) high-enthusiastic voice, narrating a multimedia lesson on distributed denial-of-service attack. While Alexa’s enthusiastic voices did not enhance persona ratings compared to Alexa’s neutral voice, learners could infer more enthusiasm expressed by Alexa’s medium-and high-enthusiastic voices than Alexa’s neutral voice. Regarding cognitive load, Alexa’s low-and high-enthusiastic voices decreased intrinsic and extraneous cognitive load ratings compared to Alexa’s neutral voice. While Alexa’s enthusiastic voices did not impact affective-motivational ratings differently from Alexa’s neutral voice, learners reported a significant increase of positive emotions from their baseline positive emotions after listening to Alexa’s medium-enthusiastic voice. Finally, Alexa’s enthusiastic voices did not enhance the learning performance on immediate retention and transfer tests compared to Alexa’s neutral voice. This study demonstrates that a modern text-to-speech voice enthusiasm can positively affect learners’ emotions and cognitive load during multimedia learning. Theoretical and practical implications are discussed through the lens of the Cognitive Affective Model of E-learning, Integrated-Cognitive Affective Model of Learning with Multimedia, and Cognitive Load Theory. We further outline this study’s limitations and recommendations for extending and widening the text-to-speech voice emotions research. 

https://link.springer.com/content/pdf/10.1007/s10639-022-11255-6.pdf

I am Alexa, your virtual tutor!: The effects of Amazon Alexa’s text-to-speech voice enthusiasm in a multimedia learning environment


Purpose
This study aims to examine if a pedagogical agent’s expressed anger, when framed as a feedback cue, can enhance mental effort and learning performance in a multimedia learning environment than expressed happiness.


Design/methodology/approach
A between-subjects experiment was conducted in which learners engaged with a multimedia learning material that taught programming algorithms, featuring a pedagogical agent who expressed anger or happiness as a feedback cue in response to the learners’ prior performance. Learners completed a self-reported scale and post-test for measuring mental effort and learning performance, respectively.


Findings
Female learners reported higher mental effort and had better learning performance when the pedagogical agent expressed anger than happiness. Male learners reported marginally lower mental effort when the pedagogical agent expressed anger than happiness.


Originality/value
This study focuses on a pedagogical agent’s expressed emotion as social information to learners. Extending from research advocating a pedagogical agent’s positive emotional expression, this study highlights the potential benefits of a pedagogical agent’s negative emotional expression, such as anger, as a cue for learners to enhance learning effort and performance in a multimedia learning environment.


Si Na Kew

Papers

Examining the motivation level of students in e-learning in higher education institution in Thailand: A case study

Learning Analytics in Online Learning Environment: A Systematic Review on the Focuses and the Types of Student-Related Analytics Data

Developing a Learning Analytics Intervention in E-learning to Enhance Students’ Learning Performance: A Case Study

I am Alexa, your virtual tutor!: The effects of Amazon Alexa’s text-to-speech voice enthusiasm in a multimedia learning environment

Can an angry pedagogical agent enhance mental effort and learning performance in a multimedia learning environment?