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The purpose of the study is to explore the various determinants of virtual communication technology (VCT) adoption among higher educational leaders by applying Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) model. Further, this study ascertains the relationship among the various determinants and measure the effects of these determinants in adoption of VCT by educational leaders.

The study was conducted by using online survey method for data collection from 380 educational leaders of India through a convenience sampling procedure. The structural equation modelling technique was adopted to assess the dimensions of VCT adoption and test the proposed model and hypotheses.

The findings revealed that performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC) and personal innovativeness (PI) have positive and significant effects on intention to use VCT (ITUVCT) and actual use of VCT (AUVCT), i.e. e-leadership communication adoption. Besides this, there is also a significant and positive effect of ITUVCT on actual usage of VCT by the educational leaders.

This study makes its contribution to the literature related to technology adoption recommended in previous research for expanding the contexts through new studies and generalizing the UTAUT3 model, specifically in the teaching–learning and administrative perspectives. The results of the study will be further helpful for the researchers and practitioners to better understand the factors that influence higher educational leaders to adopt VCT.

The authors applied the UTAUT3 model and explored the factors determining the adoption intention of VCT among educational leaders. This study is the first study that adopts UTAUT3 model in Indian educational leaders’ context.

The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy.

The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model.

The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2.

This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics.