Evaluating Remote Task Assignment of an Online Engineering Module through Data Mining in a Virtual Communication Platform Environment

E-learning has traditionally emphasised educational resources, web access, student participation, and social interaction. Novel virtual spaces, e-lectures, and digital laboratories have been developed with synchronous or asynchronous practices throughout the migration from face-to-face teaching modes to remote teaching during the pandemic restrictions. This research paper presents a case study concerning the evaluation of the online task assignment of students, using MS Teams as an electronic platform. MS Teams was evaluated to determine whether this communication platform for online lecture delivery and tasks’ assessments could be used to avoid potential problems caused during the teaching process. Students’ data were collected, and after filtering out significant information from the online questionnaires, a statistical analysis, containing a correlation and a reliability analysis, was conducted. The substantial impact of 37 variables was revealed. Cronbach’s alpha coefficient calculation revealed that 89% of the survey questions represented internally consistent and reliable variables, and for the sampling adequacy measure, Bartlett’s test was calculated at 0.816. On the basis of students’ diligence, interaction abilities, and knowledge embedding, two groups of learners were differentiated. The findings of this study shed light on the special features of fully online teaching specifically in terms of improving assessment through digital tools and merit further investigation in virtual and blended teaching spaces, with the goal of extracting outputs that will benefit the educational community.

Research on Recommendation of Personalized Exercises in English Learning Based on Data Mining

Aiming at the problems of traditional method of exercise recommendation precision, recall rate, long recommendation time, and poor recommendation comprehensiveness, this study proposes a personalized exercise recommendation method for English learning based on data mining. Firstly, a personalized recommendation model is designed, based on the model to preprocess the data in the Web access log, and cleaning the noise data to avoid its impact on the accuracy of the recommendation results is focused; secondly, the DINA model to diagnose the degree of mastery of students’ knowledge points is used and the students’ browsing patterns through fuzzy similar relationships are clustered; and finally, according to the clustering results, the similarity between students and the similarity between exercises are measured, and the collaborative filtering recommendation of personalized exercises for English learning is realized. The experimental results show that the exercise recommendation precision and recall rate of this method are higher, the recommendation time is shorter, and the recommendation results are comprehensive.

COVID-19, Older People’s Alcohol Use and Engagement With Support: A Rapid Evidence Synthesis

COVID-19, and associated restrictions, have likely impacted older people’s alcohol use and related support needs, given disrupted routines and stress increase alcohol use in older populations. This rapid evidence synthesis aimed to examine older people’s (aged 50+) alcohol use, and engagement with alcohol support services during COVID-19. Seventy-six articles were identified through systematic database searches, reporting 63 survey, five qualitative, three pilot, and five hospital admission studies; of general and service-user populations of older drinkers. Data were drawn together through narrative synthesis. Alcohol use increased for up to 32% of older people, including service-users; particularly older women. Increased use was linked to anxiety, depression and emotional distress. Decreased use was more common in some older populations; particularly Mediterranean. Barriers such as web access and safe transport affected older service-users’ engagement with support. Support to address hazardous alcohol use amongst older people must be prioritised in wake of the pandemic.

R-Loop Tracker: Web Access-Based Tool for R-Loop Detection and Analysis in Genomic DNA Sequences

R-loops are common non-B nucleic acid structures formed by a three-stranded nucleic acid composed of an RNA–DNA hybrid and a displaced single-stranded DNA (ssDNA) loop. Because the aberrant R-loop formation leads to increased mutagenesis, hyper-recombination, rearrangements, and transcription-replication collisions, it is regarded as important in human diseases. Therefore, its prevalence and distribution in genomes are studied intensively. However, in silico tools for R-loop prediction are limited, and therefore, we have developed the R-loop tracker tool, which was implemented as a part of the DNA Analyser web server. This new tool is focused upon (1) prediction of R-loops in genomic DNA without length and sequence limitations; (2) integration of R-loop tracker results with other tools for nucleic acids analyses, including Genome Browser; (3) internal cross-evaluation of in silico results with experimental data, where available; (4) easy export and correlation analyses with other genome features and markers; and (5) enhanced visualization outputs. Our new R-loop tracker tool is freely accessible on the web pages of DNA Analyser tools, and its implementation on the web-based server allows effective analyses not only for DNA segments but also for full chromosomes and genomes.

College students’ Network behavior Using data mining and feature analysis

Teachers may use advanced analytics to rapidly and correctly understand undergraduate behavior trends, especially when it comes to identifying undergraduate groupings that need to be focused on at a later time. This study uses data mining cluster analysis to analyze the constituent behavior of 3,245 undergraduates in a specific level ‘B’ institution’s college network. According to the data, there are four different undergraduate groups with different Web access features, with 350 participants using the accomplishments and other variables of their success have an influence on these students. As a result of this research, we were able to collect data on undergraduate college network activity, which may be used to aid in the development of academic advising management.

Intelligent and Adaptive Web Page Recommender System

In this manuscript, an Intelligent and Adaptive Web Page Recommender System is proposed that provides personalized, global and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: Uniformity and Recommendation strength. The system continuously tracks the user’s responses in order to adaptively switch between different recommendation-criteria in the group and personalized modes. The experimental results illustrate that the system achieved the maximum F1 measure of 83.28% on CTI dataset which is a significant improvement over the 70% F1 measure reported by Automatic Clustering-based Genetic Algorithm, the prior web recommender system.

Analysis of End User Access of Warn-on-Forecast Guidance Products during an Experimental Forecasting Task

Convection-allowing model ensemble guidance, such as that provided by the Warn-on-Forecast System (WoFS), is designed to provide predictions of individual thunderstorm hazards within the next 0–6 h. The WoFS web viewer provides a large suite of storm and environmental attribute products, but the applicability of these products to the National Weather Service forecast process has not been objectively documented. Therefore, this study describes an experimental forecasting task designed to investigate what WoFS products forecasters accessed and how they accessed them for a total of 26 cases (comprised of 13 weather events, each worked by two forecasters). Analysis of web access log data revealed that in all 26 cases, product accesses were dominated in the reflectivity, rotation, hail, and surface wind categories. However, the number of different product types viewed and the number of transitions between products varied in each case. Therefore, the Levenshtein (Edit Distance) method was used to compute similarity scores across all 26 cases, which helped identify what it meant for relatively similar vs. dissimilar navigation of WoFS products. The Spearman’s Rank correlation coefficient (R) results found that forecasters working the same weather event had higher similarity scores for events that produced more tornado reports and for events in which forecasters had higher performance scores. The findings from this study will influence subsequent efforts for further improving WoFS products and developing an efficient and effective user interface for operational applications.

Web Usage Mining by Neural Hybrid Prediction with Markov Chain Components

This paper presents and evaluates a two-level web usage prediction technique, consisting of a neural network in the first level and contextual component predictors in the second level. We used Markov chains of different orders as contextual predictors to anticipate the next web access based on specific web access history. The role of the neural network is to decide, based on previous behaviour, whose predictor’s output to use. The predicted web resources are then prefetched into the cache of the browser. In this way, we considerably increase the hit rate of the web browser, which shortens the load times. We have determined the optimal configuration of the proposed hybrid predictor on a real dataset and compared it with other existing web prefetching techniques in terms of prediction accuracy. The best configuration of the proposed neural hybrid method provides an average web access prediction accuracy of 86.95%.