Using Data Mining in Educational Administration - A Case Study on Improving School Attendance

open access articlePupil absenteeism remains a significant problem for schools across the globe with its negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96\%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, show that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools

Robotics for Distance learning: A Case Study from a UK Masters Programme

The MSc Intelligent Systems (IS) and the MSc Intelligent Systems and Robotics (ISR) programmes at De Montfort University are Masters level courses that are delivered both on-site and by distance learning. The courses have been running successfully on-site for eight years and are now in the fifth year with a distance learning mode. Delivering material at a distance, especially where there is technical and practical content, presents a challenge and in this paper we focus on some of the techniques adopted to overcome the particular challenges encountered in the delivery of Robotics modules

Evidence synthesis on the occurrence, causes, consequences, prevention and management of bullying and harassment behaviours to inform decision making in the NHS

Background Workplace bullying is a persistent problem in the NHS with negative implications for individuals, teams, and organisations. Bullying is a complex phenomenon and there is a lack of evidence on the best approaches to manage the problem. Aims Research questions What is known about the occurrence, causes, consequences and management of bullying and inappropriate behaviour in the workplace? Objectives Summarise the reported prevalence of workplace bullying and inappropriate behaviour. Summarise the empirical evidence on the causes and consequences of workplace bullying and inappropriate behaviour. Describe any theoretical explanations of the causes and consequences of workplace bullying and inappropriate behaviour. Synthesise evidence on the preventative and management interventions that address workplace bullying interventions and inappropriate behaviour. Methods To fulfil a realist synthesis approach the study was designed across four interrelated component parts: Part 1: A narrative review of the prevalence, causes and consequences of workplace bullying Part 2: A systematic literature search and realist review of workplace bullying interventions Part 3: Consultation with international bullying experts and practitioners Part 4: Identification of case studies and examples of good practic

A Hybrid Approach for Supporting Adaptivity in E-learning Environments

Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the ECA model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity in any given Learning Management System based on learners’ learning styles. Design/methodology/approach: This paper offers a brief review of current frameworks and systems to support adaptivity in e-learning environments. A framework to support adaptivity is designed and discussed, reflecting the hybrid approach in detail. A system prototype is developed incorporating different adaptive features based on the Felder-Silverman learning styles model. Finally, the prototype is implemented in Moodle. Findings: The system prototype supports real-time adaptivity in any given Learning Management System based on learners’ learning styles. It can deal with any type of content provided by course designers and instructors in the Learning Management System. Moreover, it can support adaptivity at both course and learner levels. Research limitations/implications: Practical implications: Social implications: Originality/value: To the best of our knowledge, no previous work has been done incorporating the concept of the ECA model and intelligent agents as hybrid architecture to support adaptivity in e-learning environments. The system prototype has wider applicability and can be adapted to support different types of adaptivity

A Consensus Approach to the Sentiment Analysis Problem Driven by Support-Based IOWA Majority

In group decision making, there are many situations where the opinion of the majority of participants is critical. The scenarios could be multiple, like a number of doctors finding commonality on the diagnose of an illness or parliament members looking for consensus on an specific law being passed. In this article, we present a method that utilizes induced ordered weighted averaging (IOWA) operators to aggregate a majority opinion from a number of sentiment analysis (SA) classification systems, where the latter occupy the role usually taken by human decision-makers as typically seen in group decision situations. In this case, the numerical outputs of different SA classification methods are used as input to a specific IOWA operator that is semantically close to the fuzzy linguistic quantifier ‘most of’. The object of the aggregation will be the intensity of the previously determined sentence polarity in such a way that the results represent what the majority think. During the experimental phase, the use of the IOWA operator coupled with the linguistic quantifier ‘most’ (math formula) proved to yield superior results compared to those achieved when utilizing other techniques commonly applied when some sort of averaging is needed, such as arithmetic mean or median techniques

Successes and challenges in developing a hybrid approach to sentiment analysis

This article covers some success and learning experiences attained during the developing of a hybrid approach to Sentiment Analysis (SA) based on a Sentiment Lexicon, Semantic Rules, Negation Handling, Ambiguity Management and Linguistic Variables. The proposed hybrid method is presented and applied to two selected datasets: Movie Review and Sentiment Twitter datasets. The achieved results are compared against those obtained when Naïve Bayes (NB) and Maximum Entropy (ME) supervised machine learning classification methods are used for the same datasets. The proposed hybrid system attained higher accuracy and precision scores than NB and ME, which shows its superiority when applied to the SA problem at the sentence level. Finally, an alternative strategy to calculating the orientation polarity and polarity intensity in one step instead of the two steps method used in the hybrid approach is explored. The analysis of the yielded mixed results achieved with this alternative approach shows its potential as an aid in the computation of semantic orientations and produced some lessons learnt in developing a more effective mechanism to calculating the orientation polarity and polarity intensity

IOWA & Cross-ratio Uninorm operators as aggregation tools in sentiment analysis and ensemble methods

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the field of Sentiment Analysis, a number of different classifiers are utilised to attempt to establish the polarity of a given sentence. As such, there could be a need for aggregating the outputs of the algorithms involved in the classification effort. If the output of every classification algorithm resembles the opinion of an expert in the subject at hand, we are then in the presence of a group decision making problem, which in turn translates into two sub-problems: (a) defining the desired semantic of the aggregation of all opinions, and (b) applying the proper aggregation technique that can achieve the desired semantic chosen in (a). The objective of this article is twofold. Firstly, we present two specific aggregation semantics, namely fuzzy-majority and compensatory, which are based on Induced Ordered Weighted Averaging and Uninorm operators, respectively. Secondly, we show the power of these two techniques by applying them to an existing hybrid method for classification of sentiments at the sentence level. In this case, the proposed aggregation solutions act as a complement in order to improve the performance of the aforementioned hybrid method. In more general terms, the proposed solutions could be used in the creation of semantic-sensitive ensemble methods, instead of the more simple ensemble choices available today in commercial machine learning software offerings

Modelling Execution Tracing Quality by Means of Type-1 Fuzzy Logic

CCIExecution tracing quality is a crucial characteristic which contributes to the overall software product quality though the present quality frameworks neglect this property. In the scope of this pilot study the authors introduce a process to create a model for describing execution tracing as a quality property; moreover, the performance of four different models created is compared. The process and the models presented are capable of capturing subjective uncertainty which is an intrinsic part of the quality measurement process. In addition, the possibility of linking the presented models to software product quality frameworks is also illustrated

Initiating advance care planning on end-of-life issues in dementia: Ambiguity among UK and Dutch physicians

Item does not contain fulltex