مطالب

Predicting students’ performance in a course is one of the major aims of educational data mining systems. In the present study, two and three-layer artificial neural networks (ANN) and neuro-fuzzy systems (NFS) were used to predict Iranian EFL learners’ final scores and compare them with scores given by their instructor. Sixty-six students’ scores in an English reading comprehension course comprising of five sub-scores of midterm (out of 40), quiz (out of 60), final (out of 50), class participation (out of 5) and bonus (out of 2) were used for training the systems. Two and three-layer ANNs and an NFS were trained to predict students’ final scores using training data. Researchers compared the students’ final scores given by their instructor and those achieved through the ANNs and NFS. The results showed that the NFS could predict and deliver scores that were closer to the linear sum of students’ scores. Moreover, three-layer ANN had a better performance than the two-layer ANN. According to these results, data mining techniques could deliver an accurate estimate of students' abilities in a particular course.

The current study primarily explored the link between EAP students’ L2 situational interest (SI) and academic reading transformative engagement (TE). Moreover, it explored SI and its subcomponents, triggered SI and maintained SI as predictors of TE. We also sought the role of different disciplines as moderators of the relationship between SI and TE. Finally, we investigated the big five personality traits as predictors of TE. The participants of this study were 160 undergraduate EAP students recruited through quota sampling. A quantitative research method with a correlational design was employed to conduct the study. Two questionnaires were adopted to measure academic reading TE and personality traits, and a questionnaire was adapted from math SI to English learning SI to assess the students’ L2 SI. The results revealed a significant and positive relationship between students’ L2 SI and their academic reading TE. SI significantly predicted TE, and between its subcomponents, only maintained SI strongly and significantly predicted TE. Neither the difference between the disciplinary groups in terms of students’ TE nor the interaction of the comparative disciplinary groups by SI was significant. The result indicated the agreeableness trait as the sole predictor of TE. Our findings have fruitful pedagogical implications discussed in the conclusion section.

L2 Motivational Self System (Dörnyei, 2005, 2009) holds a remarkable potency in explaining the long-term routes to L2 acquisition. Meanwhile, due to the prevalence of dynamic systems perspective, the theory has been awaited to account for the affective and cognitive aspects of L2 learning. The present study attempted to test an integrated model to examine the correlations between components of the revised L2MSS, L2 emotions of Enjoyment and Boredom, Intended Learning Effort, Vigilant and Eager L2 Use, and L2 Proficiency utilizing structural equation modeling. The data was obtained from 350 TOEFL test takers in ETS official centers in Iran. The results demonstrated the strongest prediction of Intended Learning Effort by Ought L2 Self/Own, coming next to the Ideal L2 Self/Own and Ideal L2 Self/Other. Ideal L2 Self/Own and L2 Learning Experience were positive predictors of L2 Enjoyment, whereas Ought L2 Self/Other and L2 Learning Experience had a positive and negative impact on L2 Boredom, respectively; L2 Enjoyment predicted Intended Learning Effort and Eager L2 Use positively, whereas L2 Intended Learning Effort Vigilant L2 Use were predicted negatively and positively by Boredom, respectively. Moreover, Eager L2 Use and Intended Learning Effort were positive predictors of L2 Proficiency; however, Vigilant L2 Use was conversely related to L2 Proficiency. Overall, the findings illustrate the superiority of a promotion orientation to L2 learning and the equal importance of quality and quantity of the Motivated Learning Behavior, which has significant pedagogical implications in terms of motivational and self-regulatory learning strategies in designing and managing L2 classroom tasks.