A Systematic Review of Process Modelling Methods and its Application for Personalised Adaptive Learning Systems

This systematic review work investigates current literature and methods that are related to the application of process mining and modelling in real-time particularly as it concerns personalisation of learning systems, or yet still, e-content development. The work compares available studies based on the domain area of study, the scope of the study, methods used, and the scientific contribution of the papers and results. Consequently, the findings of the identified papers were systematically evaluated in order to point out potential confounding variables or flaws that might have been overlooked or missing in the current literature. In turn, a critical structured analysis of the studies was done in order to rate the value of the stated works and the outcomes. Theoretically, the results of the investigated papers were summarized and empirically represented, in order to help draw conclusions as well as provide recommendations for future researches. Indeed, the investigations and findings from the papers show that one of the key challenges in developing personalised adaptive intelligent systems for learning is to build an effectively represented users profile, learning styles or objects, and behaviours to help support reasoning about each learner. Perhaps, the resultant information systems need to be able to describe and support real world (i.e. semantic or metadata) interpretation about the different learners, and provide effective ways to adapt the information about each user based on the existing knowledge or data especially as it concerns references to and/or discovery of the different patterns that can be found within the knowledge-base

Mining Useful Information from Big Data Models Through Semantic-based Process Modelling and Analysis

Over the past few decades, most of the existing methods for analysing large growing knowledge bases, particularly Big Data, focus on building algorithms and/or technologies to help the knowledge-bases automatically or semi-automatically extend. Indeed, a vast number of such systems that construct the said large knowledge-bases continuously grow, and most often, they do not contain all of the facts about each process instance or elements that can be found within the process base. As a consequence, the resultant process models tend to be vague or missing value datasets. In view of such challenge, the work in this paper demonstrates that a well-designed information retrieval system or the process mining (PM) methods should present the results or discovered patterns in a formal and structured format qua being interpreted as domain knowledge. To this end, the work introduces a process mining approach that supports further enhancement of existing information systems or knowledge-base through the conceptual means of data analysis. In turn, the paper proposes a semantic-based process mining and analysis method, or better still, information retrieval and extraction system - that is capable of detecting patterns or unobserved behaviours within any given knowledge base by making use of the underlying semantics or properties (metadata) that describes the available data. Thus, the proposed approach is grounded on the semantic modelling and process mining techniques. The work illustrates this method using the case study of Learning Process. The goal is to discover user interaction patterns within a learning execution environment and respond by making decisions based on the semantical analysis of the captured users data. Practically, the method applies semantic annotation and ontological representation of the learning process domain data and the resultant models in order to discover patterns automatically by means of semantic reasoning. Theoretically, the process mining and modelling method show that a way of addressing the common challenge with computational intelligent systems or methods is through an effectively well-designed and fit for purpose system that meets the requirements and needs of the intended users. In other words, this paper applies effective reasoning methods to make inferences over a process knowledge-base (e.g. learning process) that leads to an automated discovery of learning patterns or behaviour

Strategies Considered Effective by Business Educators for Quality Assurance in Business Education Programme in Universities in South-South Nigeria

The study was designed to find out the strategies considered effective by business educators for quality assurance in business education programme in universities in south-south Nigeria. Two research questions were posed and two hypotheses were tested at 0.05 level of significance. Population of the study comprised fifty four business educators in universities in south-south Nigeria. The study was on a descriptive survey design. Data were collected for the study through the administration of validated questionnaire on the respondents. The test-retest method was used to establish the reliability of the instrument and the overall correlation coefficient of 0.94 was obtained. The mean statistics were used to answer the research questions while z-test statistic and analysis of variance (ANOVA) were used to test the hypotheses. The findings of the study revealed that business educators considered Moderation of examination results and In-service training given to career academic as effective strategies for quality assurance in business education programme. Based on the findings, it was recommended among others that these strategies be properly monitored to ensure quality assurance in business education programme in universities

Linked Open Data: State-of-the-Art Mechanisms and Conceptual Framework

Today, one of the state-of-the-art technologies that have shown its importance towards data integration and analysis is the linked open data (LOD) systems or applications. LOD constitute of machine-readable resources or mechanisms that are useful in describing data properties. However, one of the issues with the existing systems or data models is the need for not just representing the derived information (data) in formats that can be easily understood by humans, but also creating systems that are able to process the information that they contain or support. Technically, the main mechanisms for developing the data or information processing systems are the aspects of aggregating or computing the metadata descriptions for the various process elements. This is due to the fact that there has been more than ever an increasing need for a more generalized and standard definition of data (or information) to create systems capable of providing understandable formats for the different data types and sources. To this effect, this chapter proposes a semantic-based linked open data framework (SBLODF) that integrates the different elements (entities) within information systems or models with semantics (metadata descriptions) to produce explicit and implicit information based on users’ search or queries. In essence, this work introduces a machine-readable and machine-understandable system that proves to be useful for encoding knowledge about different process domains, as well as provides the discovered information (knowledge) at a more conceptual level

Ontology: Core Process Mining and Querying Enabling Tool

Ontology permits the addition of semantics to process models derived from mining the various data stored in many information systems. The ontological schema enables for automated querying and inference of useful knowledge from the different domain processes. Indeed, such conceptualization methods particularly ontologies for process management which is currently allied to semantic process mining trails to combine process models with ontologies, and are increasingly gaining attention in recent years. In view of that, this chapter introduces an ontology-based mining approach that makes use of concepts within the extracted event logs about domain processes to propose a method which allows for effective querying and improved analysis of the resulting models through semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner). The proposed method is a semantic-based process mining approach that is able to induce new knowledge based on previously unobserved behaviours, and a more intuitive and easy way to represent and query the datasets and the discovered models compared to other standard logical procedures. To this end, the study claims that it is possible to apply effective reasoning methods to make inferences over a process knowledge-base (e.g. the learning process) that leads to automated discovery of learning patterns and/or behaviour

Coping strategies and academic engagement of part-time undergraduate student teachers in Nigeria

Empirical evidence is inadequate in Nigeria to understand the link between the coping strategies and academic engagement of part-time student teachers who face challenges that might impede their academic success. This study adopted the quantitative research paradigm to ascertain part-time undergraduate student teachers’ coping strategy and their academic engagement. One hundred and fifty-five (155) undergraduate part-time student teachers of Nnamdi Azikiwe University formed the sample size. Major findings showed that respondents adopted more of problem- focused coping than emotion-focused coping strategies and were academically engaged. Significant mean differences did not occur based on gender and marital status in the dimensions of coping strategies and academic engagement except in extra- curricular engagement but occurred in coping efficacy, emotional support coping and disengagement coping dimensions affective liking for school, extra-curricular engagement and cognitive engagement based on students’ specialty. Significant positive relationships occurred in almost all the dimensions of coping and academic engagement and the predictive powers of the independent variables on the dependent variables were ascertained. Demographic variables did not significantly moderate the relationship between problem-focused coping dimensions and the academic engagement while they did on emotion-focused coping and academic engagement. Conclusions were drawn and recommendations made based on the findings

A Semantic Rule-based Approach Supported by Process Mining for Personalised Adaptive Learning

Currently, automated learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. There has been a big gap between the extraction of useful patterns from data sources to knowledge, as it is crucial that data is made valid, novel, potentially useful and understandable. To meet the needs of intended users, there is requirement for learning systems to embody technologies that support learners in achieving their learning goals and this process don’t happen automatically. This paper propose a novel approach for automated learning that is capable of detecting changing trends in learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns within learning processes, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic Modelling and Process Mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to automated discovery of learning patterns or behaviour

In Defense of the Nigerian Homeland

Recently, Nigeria has experienced various acts of domestic terrorism and kidnapping that may be rooted in many forms of motivations and agitations. These aggressions have resulted in homegrown bloodshed, émigré aggression, or even organized international network assaults against the population. The scourge of these terrorist activities has continued to weaken the Nigerian character or moral fiber. While the government is trying to combat these offenses caused by extremist activities, there are still remedies that have not been implemented prudently or applied properly. In this article, we propose various forms of the repertoire of actions which the government can use to effectively fight and combat terrorism in a democratic Nigeria

Semantic-Based Model Analysis Towards Enhancing Information Values of Process Mining: Case Study of Learning Process Domain

Process mining results can be enhanced by adding semantic knowledge to the derived models. Information discovered due to semantic enrichment of the deployed process models can be used to lift process analysis from syntactic level to a more conceptual level. The work in this paper corroborates that semantic-based process mining is a useful technique towards improving the information value of derived models from the large volume of event logs about any process domain. We use a case study of learning process to illustrate this notion. Our goal is to extract streams of event logs from a learning execution environment and describe formats that allows for mining and improved process analysis of the captured data. The approach involves mapping of the resulting learning model derived from mining event data about a learning process by semantically annotating the process elements with concepts they represent in real time using process descriptions languages, and linking them to an ontology specifically designed for representing learning processes. The semantic analysis allows the meaning of the learning objects to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge which are used to determine useful learning patterns by means of the Semantic Learning Process Mining (SLPM) algorithm - technically described as Semantic-Fuzzy Miner. To this end, we show how data from learning processes are being extracted, semantically prepared, and transformed into mining executable formats to enable prediction of individual learning patterns through further semantic analysis of the discovered models