Learning Management Systems (LMSs) are increasingly utilized for the administration, tracking, and reporting of educational activities. One such widely used LMS in higher education institutions around the world is Blackboard. This is due to its capabilities of aligning items of learning content, student-student and student-teacher interactions, and assessment tasks to specified goals and student learning outcomes. This study aimed to determine how certain Key Performance Indicators (KPIs) based on student interactions with Blackboard helped to forecast the learning outcomes of students. A mixed-methods study design was used which included analysis of four deep learning models for predicting student performance. Data were collected from reports on seven general preparation courses. They were analyzed using a documentary analysis approach to establish possible predictive KPIs associated with the electronic Blackboard report. Correlational analyses were performed to examine the extent to which these factors are linearly correlated with the performance indicators of students. Results indicated that a predictive model which combined convolutional neural networks and long short-term memory (CNN-LSTM) was the optimal method among the four models tested. The main conclusion drawn from this finding is that the combined CNN-LSTM approach may lead to interventions that optimize and expand use of the Blackboard LMS in universities.

Using advanced algorithms to conduct a thematic analysis reduces the time taken and increases the efficiency of the analysis. Long short-term memory (LSTM) is effective in the field of text classification and natural language processing (NLP). In this study, we adopt LSTM for text classification in order to perform a thematic analysis using concordance lines that are taken from a corpora of news articles. However, the statistical and quantitative analyses of corpus linguistics are not enough to fully identify the semantic shift of terms and concepts. Therefore, we suggest that a corpus should be classified from a linguistic theoretical perspective, as this would help to determine the level of the linguistic patterns that should be applied in the experiment of the classification process. We suggest investigating the concordance lines of the articles rather than only the relationship between collocates, as this has been a limitation for many studies. The findings of this research work highlight the effectiveness of the proposed methodology for the thematic analysis of media coverage, reaching 84% accuracy. This method provides a deeper thematic analysis than only applying the classification process through the collocational analysis.

This is a mixed methodology study that explores language education policy (LEP) of using the first language (L1) in teaching the second language (L2). The literature points out that students’ perspective towards LEP of the preparatory year program (PYP) in Saudi universities has not been investigated before. This study engages with understanding students’ awareness about LEP of the PYP towards L1 and students perspectives towards using L1 in teaching L2. Students from all the tracks of the PYP, namely: medical, science and humanities tracks participated in this study. This study used interviews and questionnaires as tools to collect data. Data collection of this study was classified into two phases. The first phase was in 2015 and the second phase was in 2020. Data analysis of this study reveals that the majority of students in the first and second phases were not familiar with the LEP of the PYP towards L1. Data analysis points out that students’ perspective towards using L1 in teaching L2 did not correlate between the first and second phases of this study. Most of the participants in the first phase did not support using L1 in teaching L2. On the other hand, the majority of the participants in the second phase supported using L1 in teaching L2. Policy-makers in the PYP are encouraged, based on the results of this study, to adopt an “English-mainly” policy that considers students’ perspective in the LEP of the PYP and treats students as bilingual speakers of both the Arabic language and the English language.

This paper analyses how the E-Learning Unit at the university level (re)constructs instructions that facilitate the utilization of e-learning by stakeholders. More specifically, the paper presents an analysis of an assistance discourse during the University of Ha’il transition to distance learning that was prompted by the COVID-19 pandemic. The researchers suggest revising educational regulations for in-depth qualitative analysis of stakeholders’ views on policy. In so doing, they conducted various levels of analyses using two sets of data: (1) two surveys (for students and academic staff); (2) and four interviews with the coordinators of the E-Learning Unit at the university. The surveys identify the institutional context during the transition period to distance learning. The interviews are also analysed using Fairclough and Fairclough’s (2012) practical reasoning approach to clarify the strategies for supporting stakeholders. The main finding of the research is that the style of assistance discourse and the types of support should be stated clearly in university policy to enhance the effectiveness of the E-Learning Unit’s support of stakeholders.

In recent years, several research and development initiatives have focused on developing secure and trustworthy systems for the healthcare industry via pervasive and mobile healthcare (mHealth) solutions. State-of-the-art mHealth solutions primarily rely on centralized storage, such as cloud computing servers, which may escalate the maintenance costs, require ever-increasing storage infrastructure, and pose privacy and security risks to the health-critical data produced, consumed, and transmitted over ad hoc networks. To overcome these limitations, we conducted this study intending to synergize mobile computing (devices to process health-critical data) and blockchain technology (infrastructure to secure storage and retrieval of health-critical data), specifically addressing data security and privacy using a blockchain mHealth system. The research employs an incremental method by (i) developing a framework that acts as a blueprint to architect blockchain-enabled mHealth systems, (ii) implementing a suite of algorithms as a proof-of-concept to automate the framework, and (iii) experimental evaluations to validate the scalability, computation, and energy efficiency of the proposed solution. The proposed framework has been implemented as a frontend using a mobile application interface that exploits the backend via the InterPlanetary File System (IPFS) system and Ethereum blockchain for secure management of mHealth data. We use a case-study-based approach demonstrating how health units, medics, and patients can securely access and distribute health-critical data. For evaluation, we deployed a smart contract prototype on the Ethereum TESTNET network in a Windows environment to test the proposed framework. Results of the evaluation indicate (a) scalability with query response time (range: 10–41 ms), (b) computational performance (CPU utilization: 1.5% – 2.5%), and (c) energy efficiency (gas consumption: 40000 units for 1000 bytes). The proposed solution – framework, algorithms, and experimental evaluation – aims to advance state-of-the-art architecting and implementing cybersecurity mHealth solutions using blockchain technology.

Summarization of texts have been considered as essential practice nowadays with the careful presentation of the main ideas of a text. The current study aims to provide a methodology of summarizing complex texts such as argumentative discourse. Extractive and abstractive summarization techniques have recently gained significant attention. Each has its own limitations that reduce efficiency in the coverage of the main points of the summary, but by combining them, we can use the positive points of each to improve both summarization performance and summary generation quality. This paper presents a novel extractive-abstractive text summarization method that ensures coverage of the main points of the entire text. It is based on combining Bidirectional Encoder Representations from Transformers (BERT) and transfer learning. Using a dataset comprising two UK parliamentary debates, the study shows that the proposed method effectively summarizes the main points. Comparing extractive and abstractive summarization, the experiment used Recall-Oriented Understudy for Gisting Evaluation (ROUGE) sets of metrics and achieved scores of 30.1, 9.60, and 27.9 for the first debate, and 36.2, 11.80, and 31.5 for the second, using ROUGE-1, ROUGE-2, and ROUGE-L metrics, respectively.