SN‐Learning: An exploratory study beyond e‐learning and evaluation of its applications using EV‐SNL framework

Krouska, Akrivi; Troussas, Christos; Virvou, Maria

doi:10.1111/jcal.12330

Cited by 28 publications

(14 citation statements)

References 36 publications

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“…(2) The research focus on deploying AI in education is gradually shifting from a computational focus that demonstrates use cases of new technology to cognitive focus that incorporates cognition in its design, such as perception (VanRullen, 2017), emotion (Song et al, 2016), and cognitive thinking (Bramley et al, 2017). Moreover, it is also shifting from a single domain (e.g., domain expertise, or expert systems) to a cross-disciplinary approach through collaboration (Spikol et al, 2018;Krouska et al, 2019) and domain transfers (L'heureux et al, 2017). These controversial shifts are facilitating transitions from the knowing of the unknown (gaining insights through reasoning) to the unknown of the unknown (figuring out hidden values and unknown results through algorithms) (Abed Ibrahim and Fekete, 2019;Cutumisu and Guo, 2019).…”

Section: Big Data and Ai In Education: Researchmentioning

confidence: 99%

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

et al. 2020

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We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of "cold" technology and "warm" humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.

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Section: Big Data and Ai In Education: Researchmentioning

confidence: 99%

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

et al. 2020

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“…ese technological advances include the development of webbased educational methods and forms of education that use personal computers and mobile devices as learning tools. e online and offline mixed teaching method came into being under this background [12,13].…”

Section: Discussionmentioning

confidence: 99%

The Application of the Online and Offline Interactive Teaching Method in Clinical Anesthesiology Teaching

Qian

Huang

et al. 2021

Education Research International

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Objective. To explore whether better teaching effect can be achieved by applying the online and offline interactive teaching method to clinical anesthesiology teaching. Methods. Sixty-three students in grade three majoring in anesthesiology of Wenzhou Medical University were selected as research objects and randomly divided into 2 groups. The experimental group (32 students) adopted the online and offline interactive teaching method. The control group (31 students) adopted the traditional teaching method. The teaching effect was evaluated by the theoretical test results of the two groups and the survey of students in the experimental group. Results. The score of the theoretical test of students in the experimental group (79.31 ± 4.54) was higher than that of students in the control group (75.61 ± 4.88), and the difference achieved statistical significance p < 0.05 . 81.3% of the students think that the interactive teaching method enhances the interest of learning. 90.6% (29/32) of the students in the experimental group hold positive attitude towards the fact that the overall learning effect can be improved by the online and offline interactive teaching method. Conclusions. The online and offline interactive teaching method can not only improve students’ theoretical test results but also help to improve students’ learning participation. Students have given a high score for the evaluation of the online and offline interactive teaching method; good teaching effect has been achieved.

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“…One method for accomplishing this could be working in an e-learning platform, especially one that leads to higher order cognition and enhanced learning. Developing integrated educational environments using e-learning platforms that incorporate valuable features of intelligent adaptive and collaborative learning can support the learning process and assist students in their efforts while reducing the overall time commitment of instructors as well [41][42][43][44][45].…”

Section: Discussionmentioning

confidence: 99%

Service-learning Or Internship: A Mixed-methods Evaluation Of Experiential Learning Pedagogies

Sherlock

Leary

2020

Medicine &Amp; Science in Sports &Amp; Exercise

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Experiential learning pedagogies, including internship and service-learning experiences, are becoming increasingly popular in higher education. An internship engages students with hands-on experiences that enhance their learning or skills within their fields of study. In contrast, service-learning is a type of experiential education in which students participate in service, typically within the community, and reflect on their involvement to gain further understanding of the discipline as well as its relationship to societal needs. To date, no study has directly compared these teaching modalities. erefore, the present study systematically evaluated a service-learning experience against an internship experience using a mixed-methods model with the primary outcome being student self-efficacy. Sixteen exercise physiology students (13 females) completed a community-based wellness internship with a subgroup (nine students, 7 females) allocated to a service-learning component of internship designed to improve selfefficacy. At the end of the semester, students completed a 15-item online self-efficacy and satisfaction survey. ree focus groups were conducted in which 3-4 participants responded to a series of nine questions that explored their experience. Overall responses to the self-efficacy and satisfaction survey were favorable for both groups, but the internship group was more likely to agree or strongly agree with statements of self-efficacy. Focus groups found that the internship experience reinforced classroom learning, but the ability to work with different populations and ability levels was mentioned only by the service-learning group. emes from reflective assignments, such as engaging with community members and professional exploration, were evident only in the service-learning group responses. erefore, the findings indicate that service-learning reflection assignments successfully connect the service experience to relevant course outcomes promoting student development but may not improve self-reported self-efficacy beyond that of a typical internship experience.

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SN‐Learning: An exploratory study beyond e‐learning and evaluation of its applications using EV‐SNL framework

Cited by 28 publications

References 36 publications

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

The Application of the Online and Offline Interactive Teaching Method in Clinical Anesthesiology Teaching

Service-learning Or Internship: A Mixed-methods Evaluation Of Experiential Learning Pedagogies

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