Negotiating the boundaries between the formal and the informal: An experienced teacher's reflective adaptations of informal learning in a keyboard class for at-risk students

2016 ◽  
Vol 33 (3) ◽  
pp. 309-326 ◽  
Author(s):  
Pamela Costes-Onishi

The objective of this study is to address the important questions raised in literature on the intersections between formal and informal learning. Specifically, this will be discussed within the concept of ‘productive dissonance’ and the pedagogical tensions that arise in the effort of experienced teachers to transition from the formal to the informal. This case study discusses the issues that ensue when strict demarcations between formal and informal are perceived, and demonstrates that the former is vital to the facilitation of the latter. The blurring of formal and informal pedagogical approaches has shown that the concept of ‘critical musicality’ becomes more apparent in student learning and that engagement increases especially among at-risk students.

Author(s):  
Sonthya Vanichvatana

Informal learning spaces (ILS) include both inside and outside library spaces and university’s borderline. A university has its duty to provide classrooms and other supporting spaces for formal and informal learning. Nevertheless, the arrangement of such spaces might not logically and functionally match learning preferences and behaviours of students, who are prime users. The deficiency of on-campus ILS might drive students to use off-campus ILS. The understanding of why students select offcampus ILS can reflect any absence and inadequacy of on-campus ILS. The objective was to study where and why undergraduate students of business school select off-campus ILS. This research used students of a Bangkok private university as a case study. The research method was through quantitative analysis and descriptive data analysis, using questionnaire surveys conducted during March 2018. Students with any levels of grade point averages and undergraduate levels had similar preferences for using and not using off-campus ILS. Keywords: Informal learning, learning spaces, ILS, HEI, off-campus, Bangkok.


Author(s):  
Vicente Reyes ◽  
Katherine McLay ◽  
Lauren Thomasse ◽  
Karen Olave-Encina ◽  
Arafeh Karimi ◽  
...  

AbstractScholars and practitioners argue that information and communication technology (ICT) provides flexibility of time and place and softens boundaries between students’ learning lives. The fluid movement between formal and informal learning contexts afforded by digital technology has prompted a re-definition of higher education learning environments to harness its potential. Further, technology can cater to diverse learners and promote lifelong learning in ways that the traditional didactic settings characteristic of tertiary contexts cannot. Scholars and practitioners have labelled this new teaching and learning landscape as smart pedagogy. This article engages with this scholarship by analysing a specific Australian case study in which ICT reforms have been deliberately implemented to adhere to smart pedagogies. Using collective biographies as a methodological tool, this inquiry provides insights into sensemaking experiences of a group of university academics whilst implementing ICT reforms anchored on Smart Pedagogy.


2021 ◽  
Vol 48 (6) ◽  
pp. 720-728
Author(s):  
Wenting Weng ◽  
Nicola L. Ritter ◽  
Karen Cornell ◽  
Molly Gonzales

Over the past decade, the field of education has seen stark changes in the way that data are collected and leveraged to support high-stakes decision-making. Utilizing big data as a meaningful lens to inform teaching and learning can increase academic success. Data-driven research has been conducted to understand student learning performance, such as predicting at-risk students at an early stage and recommending tailored interventions to support services. However, few studies in veterinary education have adopted Learning Analytics. This article examines the adoption of Learning Analytics by using the retrospective data from the first-year professional Doctor of Veterinary Medicine program. The article gives detailed examples of predicting six courses from week 0 (i.e., before the classes started) to week 14 in the semester of Spring 2018. The weekly models for each course showed the change of prediction results as well as the comparison between the prediction results and students’ actual performance. From the prediction models, at-risk students were successfully identified at the early stage, which would help inform instructors to pay more attention to them at this point.


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