Collect, scope, and verify big data - A framework for institution accreditation

© 2016 IEEE. Institutions in higher education generate terabytesof data that has great value to shape future of nations. This Big Data is in heterogeneous formats, very current, and in large volumes. We propose a framework to collect, scope and verify this large amount of data. Although the framework is explained in the context of institution accreditation in higher education, the framework can be applied in the fields of health-care, finance, marketing etc. Our framework is used to reduce human involvement in the collection and analysis of data, for the purpose of accreditation. The framework extends the scope of data collected to target the heterogeneous nature of the data. Finally, our framework helps to verify the data against a standard set by an accreditation body

Mining Educational Data for Academic Accreditation: Aligning Assessment with Outcomes

© 2016, Global Institute of Flexible Systems Management. Institutions in higher education generate terabytes of data that has great value to shape future of nations. This Big Data is in heterogeneous formats, very current, and in large volumes. We propose a framework to collect, scope and verify this large amount of data. The analysis of the data is used to evaluate the institution against a standard set by an accreditation body, for the purpose of the academic accreditation of higher education programs. Therefore, the framework reduces human involvement in accreditation. The paper provides the detailed design of the process of aligning assessment with student learning outcomes

Development of predicting model for safety behaviour based on safety psychology and working environment

The increasing trend of occupational accident due to unsafe act and unsafe condition especially in construction site suggests the need for more proactive safety assessment model. Therefore this research aimed to establish a prediction model of safety behaviour based on safety psychology and working environment factors in construction site. Theory of Planned Behaviour (TpB) was adapted to examine on the prediction model of safety behaviour among construction workers using safety psychology representing unsafe act and working environment factors representing unsafe condition. A modified perception questionnaire named Safety Psychometric Model (SPM) was proposed based on TpB questionnaire and safety attitude questionnaire (SQA). Previously, the approach has successfully applied in health care and manufacturing sector. The questionnaire has been validated by three industrial and academic experts. A total of 554 respondents among 92 construction site were selected as the subjects for analysis. Structural Equation Modelling (SEM) and Statistical Package for the Social Science (SPSS) was use for analysis purpose which involve correlation, regression and structural equation analysis. The results demonstrated that safety psychology and work environment factor was related positively with safety behaviour intention. The elements of workers’ attitude, subjective norm and perceived control that form the safety psychology context found to be significantly has the ability to predict safety behaviour. The demographics variances of personal and education background, working experiences and training background also determine as the factors of safety behaviour of the construction workers. The research also successfully established a safety behaviour prediction model that named Safety Psychometric Model. The model can be benefited by safety practitioners, organizations and researchers to explore the safety behaviour prediction. It also enhanced the knowledge in the area of employee behaviour prediction and modelling

User Consent in MOOCs – Micro, Meso, and Macro Perspectives

While many strategies for protecting personal privacy rely on regulatory frameworks, consent, and anonymizing data, they are not always effective. Terms and Conditions often lag behind advances in technology, software, and user behaviours, and consent to use data for a range of unclear purposes may be provided unwittingly. As the commercial market for (student) data expands, so does the number of brokers who move, share and sell data across continents and legislative environments. This paper reviews four Massive Open Online Course (MOOC) providers from different geopolitical and regulatory contexts. It explores how consent to collect and use data is described to potential users, and how that consent applies at micro, meso, and macro levels. This paper proposes a need for greater transparency around the implications of users granting consent at the point of registration. Further, it highlights that though MOOC providers have a responsibility to make clear the potential uses and sharing of user data, users themselves should also be more aware and consider how meaningful student agency can be achieved

Guide to using Evidence in Higher Education

This Guide to Using Evidence has been designed to, to support and encourage students and students’ association and union staff to actively engage with data and evidence. It offers an accessible introduction to a range of key ideas and concepts and a range of activities which allow readers to develop their own thinking and confidence in key areas. The ambition of its authors, QAA Scotland and the students who reviewed early drafts, is that students and students’ association and union staff will reach for this resource as they prepare for committees, devise new campaigns, deliver services, and do all of the other things they do to enhance students’ experiences and outcomes. Underpinning all of this is a belief that students themselves, the institutions they are working with, and the sector as a whole, are better served when students are, and are seen to be, agents in the ‘data landscape’, not just subjects of it. Engaging with this Guide will help students and students’ association and union staff to develop that sense of agency in themselves and foster it in others. This Guide is a product of a student-led project coordinated by QAA Scotland as part of the Evidence for Enhancement Theme (2017-20)

Towards building a computer-aided accreditation system

Accreditation is a big subject. What is accreditation? Why should it matter to us? How many types of accreditation can an institution have? Is the government involved? What issues are present? How can we improve the accreditation process? All these questions will be covered in this paper. In addition, I will build towards a software that will apply the most important points in this paper, like applying the mission, objectives, and outcomes expected from the students in the form of a syllabus. This will help the faculty with the accreditation process and will help the students know what is expected from them since the first day of class. In addition, it will improve their performance

Modelling framework to support decision-making in manufacturing enterprises

Systematic model-driven decision-making is crucial to design, engineer, and transform manufacturing enterprises (MEs). Choosing and applying the best philosophies and techniques is challenging as most MEs deploy complex and unique configurations of process-resource systems and seek economies of scope and scale in respect of changing and distinctive product flows. This paper presents a novel systematic enhanced integrated modelling framework to facilitate transformation of MEs, which is centred on CIMOSA. Application of the new framework in an automotive industrial case study is also presented. The following new contributions to knowledge are made: (1) an innovative structured framework that can support various decisions in design, optimisation, and control to reconfigure MEs; (2) an enriched and generic process modelling approach with capability to represent both static and dynamic aspects of MEs; and (3) an automotive industrial case application showing benefits in terms of reduced lead time and cost with improved responsiveness of process-resource system with a special focus on PPC. It is anticipated that the new framework is not limited to only automotive industry but has a wider scope of application. Therefore, it would be interesting to extend its testing with different configurations and decision-making levels

The Validation of an Infrared Simulation System

A commonly-used term in the simulation domain is ‘validation, verification and accreditation’ (VVA). When analysing simulation predictions for the purpose of system solution development and decision-making, one key question persist: “What confidence can I have in the simulation and its results? ” Knowing the validation status of a simulation system is critical to express confidence in the simulation. A practical validation procedure must be simple and done in the regular course of work. A well-known and acknowledged validation model by Schlesinger depicts the interaction between three entities: Reality, Conceptual Model and Computer Model, and three processes: Analysis & Modelling, Programming and Verification, and Evaluation and Validation. We developed a systematic procedure where each of these six elements is evaluated, investigated and then quantified in terms of a set of criteria (or model properties). Many techniques exist to perform the validation procedure. They include: comparison with other models, face validity, extreme condition testing, historical data validation and predictive validation - to mention a few. The result is a two- dimensional matrix representing the confidence in validation of each of the criteria (model properties) along each of the verification and validation elements. Depending on the nature of the element, the quantification of each cell in this matrix is done numerically or heuristically. Most often literature on validation for simulation systems only provides guidance by means of a theoretical validation framework. This paper briefly describes the procedure used to validate software models in an infrared system simulation, and provides application examples of this process. The discussion includes practical validation techniques, quantification, visualisation, summary reports, and lessons learned during the course of a validation process. The framework presented in this paper is sufficiently general, so that the concepts could be applied to other simulation environments as well