Comparative Efficacy of Elsa and English Speaking Practice: A Quasi-Experimental Study on EFL Learning Outcomes

In an era where digital learning tools are reshaping the educational landscape, selecting the right language learning application becomes paramount, especially for institutions aiming to enhance their students' English proficiency. While numerous language applications exist, only a few are accessible without cost. This study zeroes in on two such free applications: ELSA and English Speaking Practice. The choice of these apps aimed to discern the instructional differences and their subsequent impact on learners. Conducted at SMK Negeri 17 Samarinda, the research involved two experimental classes: X Pharmacy 4 (33 students) and X Medical Laboratory Technology 1 (34 students). Using a Quasi-Experimental design, the study employed quantitative methods, including pre-test and post-test evaluations, to gauge the comparative effectiveness of the apps. The results were illuminating: the class utilizing ELSA witnessed a proficiency uptick of 15.21%, whereas the English Speaking Practice class observed a 13% increase. With a p-value of 0.000 <0.05, the data conclusively shows significant disparities in the efficacy of the two applications. This research underscores the importance of judiciously selecting digital tools in EFL contexts, ensuring optimal learning outcomes

Conditions and the effects of an intelligent tutoring system usage for Russian high-stakes exam in English

The aim of the proposed study was to dwell on the field of intelligent tutoring systems as applied to high-stakes exam settings in foreign languages. The main research hypothesis of this paper was the following: Does the study attempt frequency within the suggested intelligent tutoring system affect the overall students’ learning performance in preparation for the Speaking part of the Russian high-stakes exam in the English language? Addressing this research hypothesis also resulted in acquiring understanding on key stakeholders’ perception of preparation for the Russian high-stakes exam in English. Research literature was thoroughly analyzed and the suggested intelligent system was described in detail. Data was collected through a computer-based automated procedure with further randomization and sampling. As a result of the study, three cohorts of users of the intelligent tutoring system were defined. Each cohort maintained a positive study dynamics experienced through the use of the intelligent tutoring system. Also, continuous aspiration for implementing online self-training environments was identified within the majority of a foreign language teachers’ community. The framework developed for the research can be used in future research as a foundation for investigating self-regulated learning environments created for the Speaking part preparation of high-stakes exam in foreign languages

Personalization of language learning through mobile technologies

This paper aims to help education stakeholders consider how mobile learning can contribute to improving language teaching and learning by facilitating personalized learning. A high-level overview of mobile learning is presented and the role of personalization in mobile learning is explained. A number of case studies illustrate salient aspects of mobile personalized learning from research and practice. In the final section, considerations for policy and practice are discussed

Visual authoring of virtual reality conversational scenarios for e‑learning

The COVID-19 pandemic has led to face-to-face activities being developed in a virtual format that often offers a poor experience in areas such as education. Virtual Learning Environments have improved in recent years thanks to new technologies such as Virtual Reality or Chatbots. However, creating Virtual Learning Environments requires advanced programming knowledge, so this work is aimed to enable teachers to create these new environments easily. This work presents a set of extensions for App Inventor that facilitate the authoring of mobile learning apps that use Chatbots in a Virtual Reality environment, while simultaneously monitoring of student activity. This proposal is based on integrating block-based languages and Business Process Model and Notation diagrams. The developed extensions were successfully implemented in an educational app called Let’s date!. A quantitative analysis of the use of these extensions in App Inventor was also carried out, resulting in a significant reduction in the number of blocks required. The proposed contribution has demonstrated its validity in creating virtual learning environments through visual programming and modelling, reducing development complexity