Early identification of at-risk students in a lower-level engineering gatekeeper course

Vasquez, Horacio; Fuentes, Arturo; Kypuros, Javier A.; Azarbayejani, Mohammad

doi:10.1109/fie.2015.7344361

Cited by 14 publications

(11 citation statements)

References 12 publications

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“…Personalized feedback [17] is one possible intervention mechanism for these kind of approaches. Other intervention mechanisms can be applied, such as pedagogical recommendations [43], mentoring [44], or academic support environment [45], with a significant impact on performance, dropout, and retention. Feeding students depending on his or her situation will affect students' perception and will impact his or her way of learning and self-regulation.…”

Section: Early Warning Systemsmentioning

confidence: 99%

An Early Warning System to Detect At-Risk Students in Online Higher Education

2020

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Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.

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Section: Early Warning Systemsmentioning

confidence: 99%

An Early Warning System to Detect At-Risk Students in Online Higher Education

2020

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“…The pass rate in recent semesters has been around 75%. Details regarding related results can be found in [8], [9].…”

Section: Preliminary Results and Outcomesmentioning

confidence: 99%

Developing an ecosystem for student success in engineering in Rio South Texas

Kypuros¹,

Crown²,

Fuentes³

et al. 2015

2015 IEEE Frontiers in Education Conference (FIE)

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The University of Texas-Pan American (UTPA) faces some unique regional challenges that hinder retention, success, persistence, and progression. These regional challenges include reduced college preparedness, a high incidence of poverty, low educational attainment, and a language barrier. In particular, first-year, first-term UTPA students are largely unprepared to take Calculus. This places an undue burden on the students, lengthens their time to graduation, and hinders their performance in many gatekeeper courses. The shortcomings that result in gatekeeper courses are especially acute and significantly hinder success in upper-division courses. To overcome these, the authors have identified four proven practices that will accelerate mathpreparedness of incoming freshman, overcome misconceptions that hinder progression from lower to upper division, and enhance sustained student engagement throughout. Together these practices form a strategy intended to engender a sustainable ecosystem of student success in Engineering and Computer Science in the Rio South Texas Region.

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“…Low student enrollment and high attrition rates in science, technology, engineering, and mathematics (STEM) education are part of the major contemporary challenges in higher education [1]. As a consequence, introductory physics courses usually become filter courses for numerous engineering students [2], which is reflected in the historical failure rate for the first engineering physics course in our institution. In an effort to reduce academic risk, as well as to better prepare engineering students for the 21st century, a methodological change in the teaching method [3,4] was decided upon in our institution.…”

Section: Introductionmentioning

confidence: 99%

Active learning reduces academic risk of students with nonformal reasoning skills: Evidence from an introductory physics massive course in a Chilean public university

Lagubeau

Tecpan

Hernández

2020

Phys. Rev. Phys. Educ. Res.

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We present the findings of a pilot plan of active learning implemented in introductory physics in a Chilean public university. The model is research based as it considered a literature review for adequate selection and design of activities consistent with the levels of students' reasoning skills. The level of scientific reasoning is positively correlated to student success. By contrast to a control group of students following traditional lectures, we observed a significant reduction in failure rate for students that do not yet possess formal scientific reasoning. This profile of students being the majority, we conclude that implementing active learning is particularly suited to the first year of higher education in the context of a developing country. It fits the particularities of student profile and typical classroom size, leading to learning improvement and reduction of academic risk, as well as being financially sound.

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Early identification of at-risk students in a lower-level engineering gatekeeper course

Cited by 14 publications

References 12 publications

An Early Warning System to Detect At-Risk Students in Online Higher Education

An Early Warning System to Detect At-Risk Students in Online Higher Education

Developing an ecosystem for student success in engineering in Rio South Texas

Active learning reduces academic risk of students with nonformal reasoning skills: Evidence from an introductory physics massive course in a Chilean public university

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