The Effective Use of Online Resources in Improving Students’ English Grammar Skills in the EFL Classes at the Tertiary Level

Abstract

The research objective of this study was to explore the effectiveness of online resources in teaching English grammar skills to English as foreign language (EFL) students at the tertiary level. The aim was to assist EFL teachers in maximising the efficiency of their English grammar lessons through the use of online resources. The sample for the study constituted of 250 first-year English Programme students. Present simple and past simple were selected as testing items. Data were collected using students’ entry-level test scores prior to the intervention and exit-level test scores after intensive training and practise using various online resources. An important variance was identified between the results of the entry-level and exit-level tests. Also, the participants’ perceptions about the effectiveness of online resources in the language learning process were analysed using a quantitative method. The outcomes confirmed that a vast majority of the participants were in favour of integrating online resources. This research also provides valuable suggestions and potential implications for the current study.

A Collaborative Filtering Recommendation Algorithm Model Based on User Feature Transfer

“…In order to verify the effectiveness of the collaborative filtering recommendation algorithm model proposed in the study, two open data sets were used for the experiment: the AIMedia data center and the dysprosium data aggregation data set. In the AIMedia data center, the user's rating range for the flipped classroom approach in tourism management is set as [1,5]. Select 500 users randomly from the database to evaluate the flipped class of tourism management.…”

“…The division of this dataset is shown in Table 1. In the dysprosium data set, users' ratings for different types of flipped classrooms in tourism management range from [1][2][3][4][5][6]. To ensure the consistency of rating data, the study replaces the initial rating of 6 with a rating of 5.…”

“…Users will have more comments on established fields and fewer comments on unfamiliar or new fields, resulting in a cold start. If we can learn users' preferences or characteristics from established fields and apply them to recommend new fields, it will significantly reduce the issue of data sparsity in those new fields [4][5][6].…”