The Engineering Undergraduate Experience: A Qualitative Perspective

Ganguly, Sohinee; Kulkarni, Mrinmoyi; Gupta, Meenakshi

doi:10.1007/s12646-013-0202-1

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…While this may be true, attrition involves a complex set of factors that also include the attitude of the students toward engineering, their self-confidence in their skills, and the quality of their interactions with instructors and peers [1]. Ganguly et al [2] surveyed both first year and senior year students to develop an understanding of their overall impressions of engineering. In the same study, the authors found that first year students missed classes primarily due to what they perceived as unstimulating teaching style (26.89%) or in some cases a lack of sleep (16.80%).…”

Section: Introductionmentioning

confidence: 99%

One-Minute Quizzes to Identify Potential Students at Risk in Engineering Courses

Sowinski¹,

Turcotte²,

Arbez³

et al. 2018

PCEEA

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– Recent years have witnessed increased interest in reducing student attrition at universities due both to matters of improved student outcomes and the practical fiscal benefits accrued from improved retention. The reason for student attrition can be due to many contributing factors (maturity, motivation, external stresses, etc.) and may be difficult to predict. Usually the home Faculty (and sometimes even the student) is only aware of risked academic failure at the end of the semester. At this point it is generally too late for an intervention to be effective. Although there are numerous intervention models that can be used to help students, these interventions are only effective if students can be identified early in their studies. One method to address this is to assess all students as frequently as possible and in as many courses as possible to create a map of student academic engagement and performance. This paper focuses on the first phase of a long-term study to create such an early warning system to help find students at risk. The first phase consisted of using one-minute quizzes during classes and general attendance to determine the level of engagement of each student. This data was then used to try to identify students in advance that risk academic failure. It was found that students who regularly missed 30% of class within the first 5 to 8 classes provided an indication of the likelihood of failure of the student.

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Section: Introductionmentioning

confidence: 99%