Technology-enhanced Learning Platforms Research Articles

The Development of a Locally-Based Virtual Gallery Grounded in Folklore Wisdom for Enhancing Creative Thinking Skills at Sekolah Alam

This study focuses on the development of a virtual gallery media based on local wisdom of folktales, aiming to improve creative thinking abilities in Sekolah Alam. The utilization of technology in educational settings has become increasingly important in fostering innovative and engaging learning experiences. By incorporating local folklore into a virtual gallery, students can explore and appreciate their cultural heritage while enhancing their creative thinking skills. This research employed a mixed-method approach, combining qualitative and quantitative data collection methods. The results indicate that the implementation of the virtual gallery positively influenced students' creative thinking abilities, as evidenced by their improved problem-solving skills, originality of ideas, and flexibility in thinking. The findings suggest that integrating local wisdom through virtual galleries offers a promising avenue for promoting creative thinking in educational environments, particularly within the context of Sekolah Alam. Further studies could explore the long-term effects of this approach and investigate other potential benefits that may arise from incorporating local wisdom into technology-enhanced learning platforms.

The Students' Responses Toward the Use of Google Classroom for Learning Vocabulary In the Higher Education

This study was undertaken with the aim of eliciting students' perspectives concerning the utilization of Google Classroom as an instructional platform for enhancing vocabulary in the realm of higher education. Employing a descriptive quantitative methodology, the investigation was carried out among a cohort of 19 students in their fourth academic semester, all of whom were actively enrolled in the English Education Department at Blitar Islamic University. The primary research tool was a questionnaire, The finding revealed a positive inclination among students toward utilizing Google Classroom for vocabulary instruction. The study underscored the efficacy of Google Classroom in facilitating vocabulary acquisition through its interactive features, enabling seamless communication between peers and instructors for the dissemination of vocabulary materials. This is proved by the students' responses, for example;84.2% of students agreed that Google Classroom can be downloaded easily on multiple devices, and another 84.2% of students agreed that they enjoy using Google Classroom for learning vocabulary because of its free accessibility. However, an element of uncertainty persists among students concerning the potential of Google Classroom to either supplement or supplant traditional classroom settings in vocabulary learning activities. This research sheds light on the promising role of technology-enhanced learning platforms like Google Classroom in fostering vocabulary development while also recognizing the need for further exploration of their integration within broader education.

Mining Sequential Learning Trajectories With Hidden Markov Models For Early Prediction of At-Risk Students in E-Learning Environments

With the onset of online education via technology-enhanced learning platforms, large amount of educational data is being generated in the form of logs, clickstreams, performance, etc. These Virtual Learning Environments provide an opportunity to the researchers for the application of educational data mining and learning analytics, for mining the students learning behavior. This further helps them in data-driven decision making through timely intervention via early warning systems (EWS), reflecting and optimizing educational environments, and refining pedagogical designs. In this, the role of EWS is to timely identify the at-risk students. This study proposes a modeling methodology deploying interpretable Hidden Markov Model for mining of the sequential learning behavior built upon derived performance features from light-weight assessments. The public OULA dataset having diversified courses and 32 593 student records is used for validation. The results on the unseen test data achieve a classification accuracy ranging from 87.67% to 94.83% and AUC from 0.927 to 0.989, and outperforms other baseline models. For implementation of EWS, the study also predicts the optimal time-period, during the first and second quarter of the course with sufficient number of light-weight assessments in place. With the outcomes, this study tries to establish an efficient generalized modeling framework that may lead the higher educational institutes toward sustainable development.

Seamless Experience of Learning across Contexts for Chinese Vocabulary Learning: A Pilot Study

This paper delineates a pilot study on a seamless language learning design that emphasises on the seamless Chinese vocabulary learning experiences across a multitude of contexts of “at home, on campus, in the classroom, at apartment and other places”. A systematic seamless learning approach was introduced. In addition, DingTalk, a technology-enhanced learning platform in tandem with social media was adopted to enhance respondents’ Chinese vocabulary learning. Eight international students of Chinese as a foreign language (CFL) participated in this study for eight weeks, where they were permitted to utilise their mobile devices to capture vivid images and create artifacts on DingTalk, the social media platform, in conjunction with conducting a series of peer-to-peer discussions regarding the artifacts. Through the findings of the post-study reflections, interviews, and the data obtained from students upon the conclusion of the study, an overall positive influence on the student's ability to diligently leverage their spectrum of vocabulary in a multitude of contexts was evident. Furthermore, the notion of seamless language learning design has empowered students to inculcate the habit of autonomous learning and translate in-class language learning to practical real-life scenarios through their seamless learning experience across various contexts, which strongly correlates with their educational competence both in and out of the classroom.

Exploring the Relationship Between Student Engagement and Authentic Learning During COVID-19

Social Networking Sites (SNSs) such as WeChat or Facebook, can facilitate university students in learning, especially during a deadly epidemic period such as COVID-19. Student engagement is a challenging task for educators in internet-enabled technology-enhanced learning platforms. This research attempts to identify the relationship between student engagement and authentic learning during COVID-19 through the Theory of Planned Behavior (TPB) as a theoretical base. Quantitative data were collected (n = 285) using an online survey technique with the students from a recognized university in China. All six proposed hypotheses, including a moderating and two mediating variables were found to be supported. The findings indicated that constructs such as affective engagement (AE) and social engagement (SE) are significant predictors of social interaction (SI) that may lead to accomplish authentic learning task (ALTask). Further, lack of attention (LAN) was found to significantly moderate social interaction and authentic learning tasks during COVID-19.

Technology-Enhanced Learning (TEL) Platforms Among Undergraduates in Private Universities in Nigeria During Covid-19 Era

The current pandemic of COVID-19 has affected every activities of the world with educational institutions not exempted. The lockdown has also halted instructional activities in the four walls of classroom around the world. Hence, many educational institutions have resulted to embracing technology-enhanced learning (TEL) platform to sustain academic session and continue to engage in teaching-learning activities through WhatsApp, Google Classroom, Schoology and Zoom Video Conferencing tool. This study examined the awareness, access and reaction toward TEL. It is descriptive research of the survey methods and the instrument is a researcher-designed questionnaire validated by four lecturers in Al-Hikmah University, Ilorin. The questionnaire was pilot tested and the data collected was subjected to a reliability test of Cronbach Alpha which yielded 0.89. Five research questions and two hypotheses guided the conduct of this study. All research questions were answered through frequency, percentage, mean and standard deviation while hypotheses raised were test using chi-square. The findings revealed that the undergraduate student of private universities possess a high rate of awareness and can access leaning content in TEL platforms which justify the positive reaction. Hence, it is recommended that TEL should be embraced to bridge the gap created by the COVID-19 pandemic lockdown across the world.

Residents’ Training in COVID-19 Pandemic Times: An Integrated Survey of Educational Process, Institutional Support, Anxiety and Depression by the Saudi Commission for Health Specialties (SCFHS)

In late December of 2019, the outbreak of coronavirus disease (COVID-19) was first reported in the city of Wuhan, the capital of Hubei province in China, and was declared a pandemic by the World Health Organization in March 2020. Globally, as of 8 July 2020, there have been 11,669,259 confirmed cases of COVID-19, including 539,906 deaths. In Saudi Arabia, the confirmed cases have already reached 223,327, with 161,096 patients confirmed to have recovered, and 2100 deaths. This study aims to determine the effect of the COVID-19 pandemic on the training programs of the Saudi Commission for Health Specialties (SCFHS) and assess trainees’ mental health status (i.e., anxiety and depression). Trainee evaluations on training programs were also sought in order to obtain insights for strategic planning necessary for curricular modifications or improvements to address the clinical learning needs of trainees during this pandemic. The main contribution of our work is an investigation of the incidence of depression and anxiety regarding COVID-19 within the community of residents and fellows. Furthermore, we elaborate on key responsive actions towards the enhancement of the mental health of trainees. Last but not least, we propose the Saudi Commission for Health Specialties (SCFHS) Model for Residents’ Mental Health Enhancement during the COVID-19 Pandemic, which consists of five integrative value layers for medical education and training, namely: the knowledge creation process and innovation; technological capabilities for personalized medicine and patient-centric healthcare with a social impact; innovative applications of technology-enhanced learning and web-based active learning approaches for medical training and education; residents’ wellbeing and the impact of COVID-19 in strategic layers. In our future work, we intend to enhance the proposed framework with studies on trainee satisfaction and the efficiency of different technology-enhanced learning platforms for medical education.

Predicting academic performance of students from VLE big data using deep learning models

The abundance of accessible educational data, supported by the technology-enhanced learning platforms, provides opportunities to mine learning behavior of students, addressing their issues, optimizing the educational environment, and enabling data-driven decision making. Virtual learning environments complement the learning analytics paradigm by effectively providing datasets for analysing and reporting the learning process of students and its reflection and contribution in their respective performances. This study deploys a deep artificial neural network on a set of unique handcrafted features, extracted from the virtual learning environments clickstream data, to predict at-risk students providing measures for early intervention of such cases. The results show the proposed model to achieve a classification accuracy of 84%–93%. We show that a deep artificial neural network outperforms the baseline logistic regression and support vector machine models. While logistic regression achieves an accuracy of 79.82%–85.60%, the support vector machine achieves 79.95%–89.14%. Aligned with the existing studies - our findings demonstrate the inclusion of legacy data and assessment-related data to impact the model significantly. Students interested in accessing the content of the previous lectures are observed to demonstrate better performance. The study intends to assist institutes in formulating a necessary framework for pedagogical support, facilitating higher education decision-making process towards sustainable education.

Learning Analytics to Inform the Learning Design: Supporting Instructor’s Inquiry into Student Learning in Unsupervised Technology-Enhanced Platforms

Instructors may design and implement formative assessments on technology-enhanced platforms (e.g., online quizzes) with the intention of encouraging the use of effective learning strategies like active retrieval of information and spaced practice among their students. However, when students interact with unsupervised technology-enhanced learning platforms, instructors are often unaware of students’ actual use of the learning tools with respect to the pedagogical design. In this study, we designed and extracted five variables from the Canvas quiz-log data, which can provide insights into students’ learning behaviors. Anchoring our conceptual basis on the ‘influential conversational framework’, we find that learning analytics (LA) can provide instructors with critical information related to students’ learning behaviors, thereby supporting instructors’ inquiry into student learning in unsupervised technology-enhanced platforms. Our findings suggest that the information that LA provides may enable instructors to provide meaningful feedback to learners and improve the existing learning designs.

Negative Binomial Models for Visual Fixation Counts on Test Items.

With the development of technology-enhanced learning platforms, eye-tracking biometric indicators can be recorded simultaneously with students item responses. In the current study, visual fixation, an essential eye-tracking indicator, is modeled to reflect the degree of test engagement when a test taker solves a set of test questions. Three negative binomial regression models are proposed for modeling visual fixation counts of test takers solving a set of items. These models follow a similar structure to the lognormal response time model and the two-parameter logistic item response model. The proposed modeling structures include individualized latent person parameters reflecting the level of engagement of each test taker and two item parameters indicating the visual attention intensity and discriminating power of each test item. A Markov chain Monte Carlo estimation method is implemented for parameter estimation. Real data are fitted to the three proposed models, and the results are discussed.

The National Undergraduate Neuroanatomy Competition: Lessons Learned from Partnering with Students to Innovate Undergraduate Neuroanatomy Education

Undergraduates often perceive neuroscience to be a challenging discipline. As the scope of neuroscience continues to expand, it is important to provide undergraduates with sufficient opportunities to develop their knowledge and skills with the aim of encouraging the future generation of basic and clinical neuroscientists. Through our experience of developing the National Undergraduate Neuroanatomy Competition (NUNC), we have accrued an extensive volume of performance data and subjective insight into the delivery of undergraduate neuroanatomy education, which has the potential to inform how to better engage students within this field. More broadly, our group has implemented a technology enhanced learning platform alongside a peer-assisted teaching program. These achieve the dual purpose of compensating for the reduction in dedicated neuroanatomy teaching hours and encouraging undergraduates to develop an interest in the neurosciences. Here, we consider how improving the learning experience at an undergraduate level encourages further engagement in the neurosciences and the importance of this within the wider neuroscience community.

EFFECTS OF TECHNOLOGY-ENHANCED METACOGNITIVE LEARNING PLATFORM ON STUDENTS’ MONITORING ACCURACY AND UNDERSTANDING OF ELECTRICITY

The aim of this research was to explore the effect of a Technology-enhanced Metacognitive Learning Platform (TeMLP) on student’s monitoring accuracy and understanding of electricity. An interactive TeMLP was prepared on the electricity unit covering the topics of static and current electricity for 7th graders; the platform contained computer animations, science experiments, e-diaries, and metacognitive prompts. In this research, pre-test/post-test control group semi-experimental model was used. The Metacognition Scale and Essay Questions on Static and Current Electricity were used as data collection tools in this research. In addition, Essay Questions on the Learning Platform and the self-explanations of students in the learning platform database were also used in the experimental group. The pre-test and post-test comparisons regarding the Metacognition Scale for the group showed that the students in the experimental group had significantly higher post-test scores compared to control group students in terms of the control and monitoring subscales. The results of the essay questions on static and current electricity revealed an important difference between the groups favoring learning platform. The views of the students about the software support these results. The conclusions drawn by the research led to recommendations for researchers about the metacognitive prompts to be employed in technology-enhanced learning platforms. Keywords: metacognition prompt, science teaching, technology-enhanced learning.

Social Cloud Computing: an Opportunity for Technology Enhanced Competence Based Learning

This paper presents a proposal of a social cloud computing platform for supporting competence-based teaching and learning process at the School of Telematics of the University of Colima, Mexico, where professors allow the management of learning units and the conduction of students' learning activities. User acceptance testing conducted on our technology-enhanced learning platform showed a high user (student) satisfaction.

TELMA: Technology-enhanced learning environment for minimally invasive surgery

BackgroundCognitive skills training for minimally invasive surgery has traditionally relied upon diverse tools, such as seminars or lectures. Web technologies for e-learning have been adopted to provide ubiquitous training and serve as structured repositories for the vast amount of laparoscopic video sources available. However, these technologies fail to offer such features as formative and summative evaluation, guided learning, or collaborative interaction between users. MethodologyThe “TELMA” environment is presented as a new technology-enhanced learning platform that increases the user's experience using a four-pillared architecture: (1) an authoring tool for the creation of didactic contents; (2) a learning content and knowledge management system that incorporates a modular and scalable system to capture, catalogue, search, and retrieve multimedia content; (3) an evaluation module that provides learning feedback to users; and (4) a professional network for collaborative learning between users. Face validation of the environment and the authoring tool are presented. ResultsFace validation of TELMA reveals the positive perception of surgeons regarding the implementation of TELMA and their willingness to use it as a cognitive skills training tool. Preliminary validation data also reflect the importance of providing an easy-to-use, functional authoring tool to create didactic content. ConclusionThe TELMA environment is currently installed and used at the Jesús Usón Minimally Invasive Surgery Centre and several other Spanish hospitals. Face validation results ascertain the acceptance and usefulness of this new minimally invasive surgery training environment.

MAS: building an educational platform for people with intellectual disabilities and cerebral paralysis

Intellectual disability is common and lifelong, with lifetime cost. Thus, this people have been the focus of recent major national policy developments in Europe, the USA, Australia and across high-income countries. The education of these students with disabilities now can benefit from a wider range of assistive tools. Following this unstoppable trend, this paper presents MAS, a software platform aimed to help people with severe intellectual disabilities and cerebral paralysis in their learning processes. MAS, as a technology enhanced learning platform, provides several tools that supports learning and monitoring for people with special needs, including adaptative games, data processing and monitoring tools. Installed in a special needs education institution in Madrid, Spain, MAS provides special educators with a tool that improved students education processes.