Feelings and performance in the first year at university: Learning-related emotions as predictors of achievement outcomes in mathematics and statistics

Abstract

Introduction. This study examined the predictive value of four learning-related emotionsEnjoyment, Anxiety, Boredom and Hopelessness for achievement outcomes in the first year of study at university. Method.We used a large sample (N = 2337) of first year university students enrolled over three consecutive academic years in a mathematics and statistics course, from an undergraduate Economics and an International Business degree programs.Results. We first showed significant differences in the emotional experiences between the students who attended, and those who were absent from the final exam. Second, the present study found emotions to have a strong predictive value for student exam scores, particularly for learning hopelessness and a prior mathematics background. This relationship was consistent over three consecutive academic years. Sentimientos y rendimiento en el primer curso universitario: las emociones como predictores de resultados académicos en las matemáticas y la estadística Discussion ResumenIntroducción. Este estudio examinó el valor predictivo de cuatro emociones relacionadas con el aprendizaje -disfrute, ansiedad, aburrimiento y desesperación-en los resultados académicos de alumnos en primer año de carrera universitaria.Método. Usamos una muestra amplia (N = 2337) de estudiantes de primero de carrera matriculados durante tres años académicos consecutivos en un curso de matemáticas y estadística de programas de grado en Economía y Comercio International. Resultados. Primero encontramos diferencias significativas en las experiencias emocionalesentre los estudiantes que realizaron el examen final y los que no. Segundo, se encontró que las emociones tienen un elevado poder predictivo en las notas obtenidas en el examen por los alumnos, especialmente desesperación relacionada con el aprendizaje y conocimiento previo sobre matemáticas. Esta relación se mantuvo a lo largo de tres cursos académicos consecutivos.Discusión y conclusión. Se presentan recomendaciones para mejorar la práctica educativa.Palabras clave: Resultados de logro; Emociones relacionadas con el Aprendizaje; Primer año de Universidad; Matemáticas y estadística. Recibido: 02.03.15 Aceptación Inicial: 12.05.15 Aceptación final: 09.11.15

Individual Differences Related to College Students’ Course Performance in Calculus II

“…Looking specifically at the online learning environment, research has revealed that positive learning dispositions bolster student engagement (Tempelaar, Niculescu, Rientes, Giesbers, & Gijselaers, 2012;Tempelaar et al, 2015), and that student engagement and frequency of participation strongly predict (2017 Although these results are not surprising, they are important because they suggest that we may be able to use LMS data to identify students who are not engaged with course material. This affords the opportunity to do things, using the online platform, to help engage students in the material.…”

“…For example, recently a study found that learning-related emotions were strong predictors of undergraduate student 131 exam scores in a math and statistics course (Niculescu et al, 2015). In general, research across this area has found that attitudinal variables are typically weakly to moderately correlated with math achievement (e.g., meta-analyses for math anxiety and math achievement found r = -.34 for grades 5-12, Hembree, 1990; r = -.27 for grades 4-12, Ma, 1999).…”

“…Beyond these factors, we also incorporated LMS online-usage data as course performance predictors, to capture student engagement and disengagement with the online portion of the course (Cocea & Weibelzahl, 2009; Tempelaar, Rientes, & Giesbers, 2015; Gašević et al, 2015). This study represents a step toward using learning analytics in combination with cognitive and attitudinal variables to inform potential recommendation systems based on predictive models to facilitate individualized learning and bolster student success (Tempelaar et al, 2015). …”

“…This study represents a step toward using learning analytics in combination with cognitive and attitudinal variables to inform potential recommendation systems based on predictive models to facilitate individualized learning and bolster student success (Tempelaar et al, 2015).…”

“…Other studies have found direct effects of time management on achievement (Jo & Kim, 2013;Loomis, 2000). Conversely, poor time management, or procrastination, negatively influences online participation (Michinov, Brunot, Le Bohec, Juhel, & Delaval, 2011;Balduf, 2009), with the students least likely to participate also demonstrating the poorest performance (Michinov et al, 2011).Past educational research has consistently shown that the best predictor of performance is performance itself and the learning analytics field is no different (Tempelaar et al, 2015). In studies that include undergraduates taking LMS-supplemented courses, the strongest predictor of final exam grades, when accounting for a variety of student dispositional and engagement variables, was grades on formative quizzes (Huon et al, 2007;Tempelaar et al, 2015).…”