Characterizing Engineering Learners’ Preferences for Active and Passive Learning Methods

Magana, Alejandra J.; Vieira, Camilo; Boutin, Mireille

doi:10.1109/te.2017.2740203

Cited by 41 publications

(19 citation statements)

References 31 publications

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“…The results about the favourable perception are similar to those reported by the most authors quoted above for comparable 'flipped' approaches -for example Wanner and Palmer (2015), whose students preferred this type of blended learning and enjoyed the increased flexibility in their learning; Yelamarthi and Drake (2015), with a significant improvement in students' perceptions of their learning experience; O'Flaherty and Phillips (2015), whose students generally reported positive perceptions, except for a significant minority having some negative views (as in our case); Baepler, Walker, and Driessen (2014), with an improved perception of the learning environment; Kim et al (2014), with students generally satisfied and agreeing in many cases that the class time interaction was helpful to their understanding of concepts, and Magana, Vieira and Boutin (2018) From the lecturers' point of view, this out-of-class activity had advantages and drawbacks. As an essential advantage, the entire scheduled syllabus could be covered with no stressful fast-paced lectures, which addresses the problems mentioned in the Introduction.…”

Section: Discussionmentioning

confidence: 58%

Independent learning as class preparation to foster student-centred learning in first-year engineering students

Pejuan

Antonijuan

2019

Research in Post-Compulsory Education

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Section: Discussionmentioning

confidence: 58%

Independent learning as class preparation to foster student-centred learning in first-year engineering students

Pejuan

Antonijuan

2019

Research in Post-Compulsory Education

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“…Suwal and Singh [10] found that the teaching of BIM tools relies on the students having a positive learning sentiment. Magana et al [11] stated that active learning should be implemented in engineering education to motivate students' learning to achieve better learning effectiveness.…”

Section: Bim Educationmentioning

confidence: 99%

“…The concept of active learning has, therefore, gained increasing attention over recent years [13]. Compared with passive learning, active learning requires students to participate in visible actions instead of merely sitting, listening, and reading [11]. Active learning has also been shown to facilitate the long-term retention of information, develop problem-solving abilities, and motivate students' interests [14].…”

Section: Active Learning In Engineering Educationmentioning

confidence: 99%

A Peer Review System for BIM Learning

Tsai

2019

Sustainability

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This research proposed a peer review system for the instruction of building information modeling (BIM). With the rapid growth of BIM technology, 3D modeling has become an essential skill for civil engineers. However, it is difficult for an engineer to gain 3D modeling skills through conventional lecture-based learning. The peer review approach has been recently introduced as an educational method and is widely utilized to promote learners' active learning that is hard to teach using conventional approaches; however, no peer review system has been developed thus far for teaching 3D modeling. Therefore, this research developed a peer review system for 3D modeling courses and proposed guidelines for developing a peer review-based BIM course. Additionally, this study developed a web-based reviewing system, which allows the learner to review others' works online. The system was implemented in an undergrad-level course for validation. The results revealed that using the peer review system for 3D modeling instruction can help learners to gain more detailed skills; it also allows learners to learn from others' works. The system can also help the instructor to identify potential mistakes that the students may make while constructing the BIM model and to make continuous improvements for future courses.

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“…MALL involves the support of students’ language learning with the increased use of mobile technologies and handheld devices. However, digital learning software and specifically MALL applications are oriented to a heterogeneous group of learners having different knowledge background, preferences, needs and interests [ 2 ]. Therefore, the emerging challenge is to develop learning technology systems that will be enriched with intelligent techniques in order to offer dynamic adaptation to students’ personal traits (such as knowledge, needs and preferences) and create a personalized learning experience.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

Troussas

Krouska

Sgouropoulou

et al. 2020

Entropy

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Mobile personalized learning can be achieved by the identification of students’ learning styles; however, this happens with the completion of large questionnaires. This task has been reported as tedious and time-consuming, causing random selection of the questionnaires’ choices, and thus, erroneous adaptation to students’ needs, endangering knowledge acquisition. Moreover, mobile environments render the selection of questionnaires’ choices impractical due to confined mobile user interfaces. In view of the above, this paper presents Learnglish, a fully developed mobile language learning system incorporating automatic identification of students’ learning styles according to the Felder-Silverman model (FSLSM) using ensemble classification. In particular, three classifiers, namely SVM, NB and KNN, are combined based on the majority voting rule. The major innovation of this task, apart from the ensemble classification and the mobile learning environment, is that Learnglish takes as input a minimum number of personal (i.e., age and gender) and cognitive characteristics (i.e., prior academic performance categorized using fuzzy weights), and solely four questions pertaining to the FSLSM dimensions, to identify the learning style. Furthermore, Learnglish incorporates adapted instructional routines to create an individualized learning environment based on students’ learning preferences as determined by their style. Learnglish was fully evaluated with very encouraging results.

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Characterizing Engineering Learners’ Preferences for Active and Passive Learning Methods

Cited by 41 publications

References 31 publications

Independent learning as class preparation to foster student-centred learning in first-year engineering students

Independent learning as class preparation to foster student-centred learning in first-year engineering students

A Peer Review System for BIM Learning

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

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