Antecedents and Correlates of Course Cancellation in a University “Drop and Add” Period

Babad, Elisha; Icekson, Tamar; Yelinek, Yaacov

doi:10.1007/s11162-007-9082-3

Cited by 19 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, students may register in several courses in order to gauge their interest in the subject matter. Upon attending a few lectures, students are then able to make an informed decision about whether or not to continue in a given course (Babad et al 2008). However, the students in this study were approximately two months into their Introductory Psychology course (i.e., over a quarter finished the course), and had received feedback on at least one in-class exam.…”

Section: Course Failurementioning

confidence: 99%

Attributional Retraining: reducing the likelihood of failure

et al. 2010

View full text Add to dashboard Cite

Failing a course is an acutely negative event for first-year university students, and a major contributor to high attrition rates at North American universities. Despite its prevalence, course failure receives relatively little research attention. What can be done to reduce course failure and help first-year students remain in university? This study examined the efficacy of an Attributional Retraining treatment intervention to reduce course failure in an Introductory Psychology course. Attributional Retraining is designed to restructure students' causal explanations of poor performance by encouraging controllable attributions such as effort and strategy in place of immutable causes such as academic ability or intelligence. Relative to students in the control group, first-year students who received Attributional Retraining were less likely to fail the Introductory Psychology course (14.6% vs. 6.4%). This finding emerged beyond the effects of several well-established predictors of academic outcomes including student background characteristics (i.e., age, gender, and past academic performance) and learning environment variables (student registration status and participation in a first-year orientation program), suggesting the utility of Attributional Retraining for students with varying backgrounds and in different educational contexts. To the extent that Attributional Retraining is effective, inexpensive, and relatively easy to 123 76 T. L. Haynes Stewart et al.administer, it may be a viable option for inclusion in orientation programs designed to reduce course failure and attrition among first-year university students.

show abstract

Section: Course Failurementioning

confidence: 99%

Attributional Retraining: reducing the likelihood of failure

et al. 2010

View full text Add to dashboard Cite

show abstract

“…A Google Scholar search surprisingly indicated that Babad, Icekson, and Yelinek (2008)conducted the only study focusing on how students make a decision to take or drop a course based on syllabus content. The authors investigated antecedents and correlates of course cancellationduring the add/drop period, the first 2-3 weeks of each term.…”

Section: Synthesizing the Literaturementioning

confidence: 99%

Taking or Not Taking a Class: Students’ Perceived Physiognomies Associated with Syllabi

Motameni¹,

Rice²,

LaRosa³

2015

IJMS

View full text Add to dashboard Cite

This paper has tried to reconcile and synthesize the existing literature on course syllabi and create an interconnected and cohesive conceptual framework capable of bringing together the findings of previous conceptualization. The spin-off objectives involved conducting a survey to establish the students' overall perceptions regarding the course syllabi in general and the most recent syllabi they have been exposed to in particular. More importantly, we wanted to find out if the students' current perceptions were significantly different from an ideal syllabus perception and whether students' gender, ethnicity, academic standing, option, time of class, and class structure (i.e., regular vs. totally online) influenced students' perception of a syllabus. This study identified four factors contributing to an ideal syllabus. This study extended the previous research on syllabi because no other study has focused on syllabus format and its visual presentation. The findings suggest that alternative strategies should be used to communicate syllabus information.

show abstract

“…Guessing the potential grade, they would get from the course would provide them guidance in course selection. As noted above, student course selection modelling is a multivariate, nonlinear one (Babad, Icekson & Yelinek, 2008;Babad & Tayeb, 2003;Babad, 2001). Neural network methods, on the other hand, are robust tools for the solution of non-linear problems (Kardan et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Student Consultancy Service: Prediction of Course Grades in Course Selection Phases Using Artificial Intelligence Techniques

Kaynak

Evirgen

2018

Journal of Computer and Education Research

View full text Add to dashboard Cite

Universities offer technical elective courses to allow students to improve themselves in various parts of their majors. Each semester, the students make a decision regarding these technical electives, and the most common expectations students have in this context include, getting education at a better school, getting a better job, and getting higher grades with a view to securing admission into more advanced degree programs. Electing a course on the basis of the interests and skills of the student will naturally translate into achievement. Advisors, in this context, play a major role. Yet, the substantial workload advisors have already assumed prevent them dedicating enough time for exploring the interests and skills of the students, and hence hinder the development of the required relationship between students and their advisors. This study attempts to estimate the achievement level a student intends to elect, on the basis of graduate data received from the database of students of Sakarya University, Faculty of Computer and Information Sciences, and led to the development of a decision-support system. The application used ANFIS and artificial neural network methods among the artificial intelligence techniques, alongside the linear regression model as the mathematical model, whereupon the performance of the methods were compared over the application. In conclusion, it was observed that artificial intelligence techniques provided more relevant results compared to mathematical models, and that, among the artificial intelligence techniques feed forward backpropagation neural network model offered a lower standard deviation compared to ANFIS model.

show abstract

Antecedents and Correlates of Course Cancellation in a University “Drop and Add” Period

Cited by 19 publications

References 28 publications

Attributional Retraining: reducing the likelihood of failure

Attributional Retraining: reducing the likelihood of failure

Taking or Not Taking a Class: Students’ Perceived Physiognomies Associated with Syllabi

Student Consultancy Service: Prediction of Course Grades in Course Selection Phases Using Artificial Intelligence Techniques

Contact Info

Product

Resources

About