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Affective factors can play an important role in learning and may change over time. This paper investigates emotional states of first-year engineering students towards self-study experience through words associated with affect used in their reflections in eportfolios over a semester. The study employed a corpusbased analysis by focusing on keywords of affect with the statistical criteria and analyzing the context those keywords occurred in. To uncover the students' recurrent feelings, the concordance lines for each keyword were further analyzed qualitatively to identify each of them into a category of pre-determined affective factors. Then, relative frequencies of each instance of each category were quantitatively counted for comparison. The findings show that affective factors in questions - namely motivation, self-efficacy, and anxiety, either positive or negative, changed with regards to frequency and direction in the different phases of learning. In sum, this study not only sets a practical model for analyzing extensive data quantitatively, but also can provide more insightful results combined with awareness-raising for teachers regarding the fluctuation of affective factors over a period of time.



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