An Analysis of the Learning Effects and Differences of College Students Using English Vocabulary APP

Abstract

With the gradual popularization of mobile learning, it has become a trend for college students to use learning applications (APPs) for learning, but the learning effect has always been a concern. Since college students have different learning purposes, strategies, skills, and habits, responses to these differences have been an urgent problem. This paper takes college students using an English vocabulary APP to study as the survey object. It sets up the influencing factors model of the English vocabulary learning effect based on UTAUT2. Then, we analyzed the factors influencing the learning effect of using an English vocabulary APP and studied its mechanism. The results show that on the one hand, the influence degree of influencing factors is habit, facilitation condition, price value, effort expectancy, and performance expectancy from high to low, and all the above factors have a significant positive impact on the learning effect of college students using English vocabulary APP. On the other hand, gender, grade, and major have a moderating effect on English vocabulary learning, and there are differences among different genders, grades, and majors. Finally, suggestions are put forward from the perspective of APP construction and students’ differences to enhance and improve the learning effect of English vocabulary.

A study on factors influencing Chinese undergraduate EFL learners’ self-directed use of mobile English learning resources

“…Effort expectancy was the second most important predictor of behavioral intention toward using MELR. This is consistent with other findings in mobile English learning context ( Mekhzoumi et al, 2018 ; Guo and Li, 2022 ) and in mobile learning in general ( Nassuora, 2013 ; Fagan, 2019 ). This finding implies that students who perceive MELR easy to use will have more positive intentions to adopt MELR for English learning.…”

“…They found that attitude and performance expectancy had significant effects on learners’ behavioral intentions toward using mobile-assisted language learning, while facilitating conditions exerted no direct influence on learners’ MALL acceptance. In terms of English vocabulary learning, Guo and Li (2022) suggested that performance expectancy, effort expectancy, price value, facilitation condition, and habit had a significant positive impact on the use of English vocabulary APP learning by college students. Using a different model (TAM), Kim and Lee (2016) investigated Korean students’ adoption of mobile-assisted language learning and potential factors affecting their MALL use.…”

A comparative study of the effects of social media and language learning apps on learners’ vocabulary performance

“…The results of the Tukey post hoc test demonstrated a statistically significant difference between the mean scores of the vocabulary post-test of the students in the three groups ( p < 0.05). This demonstrates that the language learning app is more effective at helping students’ learn vocabulary than social media for a majority of language learners, thanks to its diversified presentation of content, such as picture memory, audio memory, association memory, homophonic memory (Guo & Li, 2022 ). It is in line with Roohani and Vincheh’s finding ( 2021 ), in which three approaches—language learning apps, social media, and traditional classroom instruction—were compared, and the language learning app Phrasal Nerds : Phrasal Verbs was found to be the most effective method of the three.…”

“…To a certain extent, the grasp of vocabulary determines language understanding. Vocabulary plays an essential role in language learning, and communication will be confusing if vocabulary is inadequate or misused (Cons, 2012 ; Guo & Li, 2022 ). Vocabulary has been viewed as a vital aspect of second language teaching and learning and as important as the four main basic skills of reading, writing, listening, and speaking (Darancık, 2018 ; Li & Hafner, 2022 ).…”

Determinants of College Students’ Online Fragmented Learning Effect: An Analysis of Teaching Courses on Scientific Research Software on the Bilibili Platform

“…Based on the four hypotheses described in Section 3.2, drawing on relevant results from the existing literature on the design of learning effect scales [22,30,40] and combining these with the characteristics of TCSRS on Bilibili and the actual situation of college students' online learning, we designed the Questionnaire on College Students' Fragmented Learning of Scientific Research Software on Bilibili. In addition to the demographic information of the participants and the frequency of fragmented learning TCSRS on Bilibili, this questionnaire also includes measurement scales for five research variables, namely LM, SE, KF, FTU, and OFLE, for a total of 19 items.…”