Using Decision Tree Analysis to Understand Foundation Science Student Performance. Insight Gained at One South African University

Kirby, Nicola F.; Dempster, Edith R.

doi:10.1080/09500693.2014.936921

Cited by 10 publications

(5 citation statements)

References 28 publications

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“…, 2009 ). The technique has been used recently by various authors for performance analysis of student populations ( Kirby and Dempster, 2014 , 2015b ; Baran and Kilic, 2015 ). The CHAID model creates a tree diagram by identifying the most important predictor variables associated with the outcome variable and then splits the first predictor variable into groups that are significantly different from one another.…”

Section: Description Of the Data Setmentioning

confidence: 99%

Learning Strategies for First-Year Biology: Toward Moving the “Murky Middle”

Kritzinger

Lemmens

Potgieter

2018

LSE

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Higher education faces the challenge of high student attrition, which is especially disconcerting if associated with low participation rates, as is the case in South Africa. Recently, the use of learning analytics has increased, enabling institutions to make data-informed decisions to improve teaching, learning, and student success. Most of the literature thus far has focused on “at-risk” students. The aim of this paper is twofold: to use learning analytics to define a different group of students, termed the “murky middle” (MM), early enough in the academic year to provide scope for targeted interventions; and to describe the learning strategies of successful students to guide the design of interventions aimed at improving the prospects of success for all students, especially those of the MM. We found that it was possible to identify the MM using demographic data that are available at the start of the academic year. The students in the subgroup were cleanly defined by their grade 12 results for physical sciences. We were also able to describe the learning strategies that are associated with success in first-year biology. This information is useful for curricular design, classroom practice, and student advising and should be incorporated in professional development programs for lecturers and student advisors.

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Section: Description Of the Data Setmentioning

confidence: 99%

Learning Strategies for First-Year Biology: Toward Moving the “Murky Middle”

Kritzinger

Lemmens

Potgieter

2018

LSE

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“…However, Poole et al (2007) affirms that admission criteria are significant predictors of academic performance in the first two years of undergraduate study in dentistry but dwindle in later years. In total contrast, studies such as Bone and Reid (2011) report that no relationship exists between prior academic achievement and academic success on undergraduate programmes in medicine and biology respectively, while Kirby and Dempster (2014) attest that providing accommodation and financial support are vital predictors of academic success on a foundation programme in a South African university. Roberts (2007) concludes that cognitive style might be a better predictor of students' ability to attain academic success irrespective of whether a student had previously studied arts or science.…”

Section: Prior Academic Performance and Success On Undergraduate Progmentioning

confidence: 96%

Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

Aluko

Adenuga

Kukoyi

et al. 2016

CEB

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In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria.

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“…In education, very few studies have employed the decision tree method. Among the few studies found was Kirby and Dempster (2014), in which they identified assessment problems in a university program through decision trees, proving school performance indicators as less than satisfactory indicators for students' academic potential. Another study utilized the categorizing feature of the decision tree method to construct a well-organized knowledge structure for science learning (Hwang, Chu, Shih, Huang, & Tsai, 2010).…”

Section: Decision Tree Model and The Tree-forest Modelmentioning

confidence: 99%

Relationships between Facial Expressions, Prior Knowledge, and Multiple Representations: a Case of Conceptual Change for Kinematics Instruction

Liaw

Chou

et al. 2020

J Sci Educ Technol

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Kinematics is an important but challenging area in physics. In previously published works of the current research project, it was revealed that there is a significant relationship between facial microexpression states (FMES) changes and conceptual conflictinduced conceptual change. Consequently, the current study integrated FMES into a kinematics multiple representation instructional scenario to investigate if FMES could be used to help construct students' conceptual paths, and help predict students' learning outcome. Analysis revealed that types of students' FMES (neutral, surprised, positive, and negative) were important in helping instructors predict students' learning outcomes. Findings showed that exhibiting negative FMES through all three major representation segments of the instructional process (i.e., scientific demonstration, textual instruction, and animated instruction) suggests a higher probability of conceptual change among students with sufficient background knowledge on the topic. For students with insufficient prior knowledge, the result was the opposite. Moreover, animated representation was found to be critical to the prediction of student conceptual change. In sum, the results showed FMES as a viable indicator for conceptual change in kinematics, and also reaffirmed the importance of prior knowledge and representations of scientific concepts.

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Using Decision Tree Analysis to Understand Foundation Science Student Performance. Insight Gained at One South African University

Cited by 10 publications

References 28 publications

Learning Strategies for First-Year Biology: Toward Moving the “Murky Middle”

Learning Strategies for First-Year Biology: Toward Moving the “Murky Middle”

Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

Relationships between Facial Expressions, Prior Knowledge, and Multiple Representations: a Case of Conceptual Change for Kinematics Instruction

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