Design of an Effective Evaluation System for English Fragmented Learning Based on Mobile Information Environment

Abstract

In recent years, the fragmented learning mode of college English is the product of the integration of English teaching reform and information technology. Because of the mobile information environment, how knowledge is presented and distributed has altered significantly. As research on current college students continues to expand, the concept of fragmented learning is being utilized more and more frequently. Fragmented learning is defined as having features of fragmentation in time, space, content, and method of learning. Although fragmented learning makes it easier for college students to study, besides it also has several drawbacks, such as fragmented knowledge being scattered, no structure is developed, fragmented content being jumbled, and fragmented learning being flashy and merely a formality. To overcome these issues, this research work examines the evaluation system for the fragmented learning effect. In addition, it studies the effect evaluation of fragmented English learning in the mobile information environment and uses the integrated BPNN and genetic algorithm to evaluate the effect of fragmented English learning. The results show that this method has a strong effect on the evaluation of fragmented English learning in colleges and universities.

Antecedents of College Students’ Continuance Behaviors in Online Fragmented Learning: An Empirical Analysis from the Extended ECM Perspective

“…By doing this way, it can expand the explanatory power and scope of application of the ECM in the field of online fragmented learning. Second, this research model investigates the antecedents of college students' CBOFL and their mechanisms, extending the stream of literature on online fragmented learning [4,6,24,25]. The results can provide an academic reference for guiding the promotion of college students' CBOFL and the formation of their online fragmented learning habits in the future.…”

“…Song et al [4] investigated the mediating role of the Internet on the influence of personal characteristics, learning motivation, learning, and resources' input on the learning effect. Yang [24] constructed the evaluation system for the fragmented learning effect and evaluated the impact of fragmented English learning in the mobile information environment through the integrated BPNN and genetic algorithm. Li et al [6] found that learning motivation, self-efficacy, and fragmented time utilization influence college students' online fragmented learning effect significantly, while knowledge fragmentation has no significant impact.…”

“…However, certain limitations persist. First, although prior studies have investigated the determinants of the effect or outcome of online fragmented learning from aspects of learners' characteristics, features of courses, and platforms [5,6,23,24], the antecedents of continuance behaviors in online fragmented learning have been largely ignored. Recently, fewer studies have begun to focus on the factors influencing students' continuance intention for fragmented learning purposes [25].…”

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

“…In recent years, scholars have explored fragmented learning and its effect on learning. For example, based on using the BPNN algorithm and studying genetics, Yang [31] found that English fragmented learning has a better effect in the mobile information environment but also found that online fragmented learning has disadvantages, such as knowledge dispersion and a lack of system structure. However, Li et al [32] believe that although fragmented learning provides more possibilities for online learning, it is still necessary to ensure that all the contents of a certain knowledge point are learned over a long time to guarantee the online learning effect.…”

“…College students are affected by the school curriculum, and their time is divided into several irregular segments, characterized by "time fragmentation" [39]. In order to improve the learning ability of college students, they can use fragmented time to find fragmented content for decentralized learning, which breaks the time and space limitations of traditional learning and provides more possibilities for online learning [31]. Therefore, this paper proposes the following hypotheses based on KF and FTU: Hypothesis 3.…”