A guideline-based approach to support the assessment of students’ ability to apply object-oriented concepts in source code

There are many approaches in assessing students’ ability in object-oriented (OO) programming, but little is known on how to assess their ability in applying OO fundamental concepts in their written source codes. One major problem with programming assessment relates to variation in marks given by different assessors. Often, the grades given also does not gauge whether students know how to apply OO approaches. Thus, a new assessment approach is needed to fill these gap. The objective of this study is to construct and validate through expert consensus, a set of evaluation criteria for fundamental OO concepts together with the guidelines called GuideSCoRE, to help instructors assess students’ ability in applying OO concepts in their program source code. The evaluation criteria are derived from fundamental OO concepts found in Malaysian OO programming syllabuses and validated by a three-round Delphi approach. The proposed evaluation criteria were mapped with related OO design heuristics and OO design principles. A guideline (GuideSCoRE), constructed based on the Goal-Questions-Metrics approach together with the evaluation criteria is used by instructors when assessing students’ source codes. An inter-rater reliability analysis among six instructors found moderate agreement on assessment scores (κ values of mainly between 0.421 and 0.575) indicating that whilst the guidelines do not completely eliminate variations between raters, it help reduce their occurrences

A guideline-based approach to support the assessment of students' ability to apply object-oriented concept in source code

There are many approaches in assessing students’ ability in object-oriented (OO) programming, but little is known on how to assess their ability in applying OO fundamental concepts in their written source codes. One major problem with programming assessment relates to variation in marks given by different assessors. Often, the grades given also does not gauge whether students know how to apply OO approaches. Thus, a new assessment approach is needed to fill these gap. The objective of this study is to construct and validate through expert consensus, a set of evaluation criteria for fundamental OO concepts together with the guidelines called GuideSCoRE, to help instructors assess students’ ability in applying OO concepts in their program source code. The evaluation criteria are derived from fundamental OO concepts found in Malaysian OO programming syllabuses and validated by a three-round Delphi approach. The proposed evaluation criteria were mapped with related OO design heuristics and OO design principles. A guideline (GuideSCoRE), constructed based on the Goal-Questions-Metrics approach together with the evaluation criteria is used by instructors when assessing students’ source codes. An inter-rater reliability analysis among six instructors found moderate agreement on assessment scores (κ values of mainly between 0.421 and 0.575) indicating that whilst the guidelines do not completely eliminate variations between raters, it help reduce their occurrence

Frezhub: From farm to fork

This study reveals in order to avoid the dumping of farm produce and loss of regular market during the pandemic crisis, smallholder farmers demonstrate alternative distribution strategies to sustain their business, including distributing their produce through the local runner and marketing via social media. This alternative supply chain embraces the concept of the hub chain model, which provide insight into the conceptualisation of an integrated chain called FrezHub. The novelty of FrezHub is the use of ‘hub agent’ in the chain to replace multiple intermediaries and serve as ‘just-in-time hub’ between farmers and customers

Fixed vs. Self-Adaptive Crossover First Differential Evolution

Although the Differential Evolution (DE) algorithm is a powerfuland commonly used stochastic evolutionary-based optimizer for solvingnon-linear, continuous optimization problems, it has a highly uncon-ventional order of genetic operations when compared against canonicalevolutionary-based optimizers whereby in DE, mutation is conductedfirst before crossover. This has led us to investigate both a fixed aswell as self-adaptive crossover-first version of DE, of which the fixedversion has yielded statistically significant improvements to its perfor-mance when solving two particular classes of continuous optimizationproblems. The self-adaptive version of this crossover-first DE was alsoobserved to be producing optimization results which were superior thanthe conventional DE

Unsupervised Text Feature Extraction for Academic Chatbot using Constrained FP-Growth

In the edge where conversation merely involves online chatting and texting one another, an automated conversational agent is needed to support certain repetitive tasks such as providing FAQs, customer service and product recommendations. One of the key challenges is to identify and discover user’s intention in a social conversation where the focus of our work in the academic domain. Our unsupervised text feature extraction method for Intent Pattern Discovery is developed by applying text features constraints to the FP-Growth technique. The academic corpus was developed using a chat messages dataset where the conversation between students and academicians regarding undergraduate and postgraduate queries were extracted as text features for our model. We experimented with our new Constrained Frequent Intent Pattern (cFIP) model in contrast with the N-gram model in terms of feature-vector size reduction, descriptive intent discovery, and analysis of cFIP Rules. Our findings show significant and descriptive intent patterns was discovered with confidence rules value of 0.9 for cFIP of 3-sequence. We report an average feature-vector size reduction of 76% compared to the Bigram model using both undergraduate and postgraduate conversation datasets. The usability testing results depicted overall user satisfaction average mean score is 4.30 out of 5 in using the Academic chatbot which supported our intent discovery cFIP approach

Utilization of mobile web application for restaurant food ordering with delivery tracking status

Been relying on either pen and paper or call-in orders, which is inefficient, error-prone, time-consuming, and labour-intensive. Handwritten orders leave a lot of room for potential human errors. Taking the wrong orders or the staff’s poor handwriting, for example, may create confusion in the kitchen, lead to food and labour wastage, and increase the unnecessary budget. There are some native apps developed to tackle these issues on both Android and iOS platforms. These apps merely transform the practice from a paper-based ordering system to an online ordering system, which improves and refines customers’ experiences when they place orders. However, the process of developing apps that support multiple platforms is complex, laborious, and expensive. This paper showcases the use of a mobile web application, equipped with tracking status to allow a customer to place an online order, check the order status, and track the delivery. To develop the system, the combination of Agile and Waterfall models was used. The System Usability Scale (SUS) was used to test the usability of this mobile web application. The mobile web app can help restaurant owners switch to an online ordering system by adding their interactive online menu to the app, integrating payments, and organizing delivery, while the customers can browse through the menu, place online orders, and track the deliverie

Beacon-integrated Attendance App

Taking attendance in class is still practised in many institutions of higher learning. With the advent of technologies, the conventional practice with pen-and-paper is deemed inefficient and time consuming. On top of the possible human-error in taking the attendance and also the ease of tampering with the data, manually taking attendance is very time-consuming. There are many apps developed to tackle these issues. However, many of these apps merely transform the practice from physical pen-and-paper to electronic touch-and-click in which the attendance is still taken by the instructor. In this paper, we propose the use of a beacon device to verify the attendance and it can be configured for automatic attendance taking. On top of that, other functionalities such as attendance report, submission of letter of absent, assign demonstrator/tutor to take attendance, manual attendance for those without smartphones are also included

Exploring Edge-Based Segmentation Towards Automated Skin Lesion Diagnosis

Automated medical diagnosis has many potentials and benefits to support healthcare. Therefore, there is growing number of research on this topic. There are many challenges before automated medical diagnosis is accepted by the healthcare industry and the public as a tool to facilitate healthcare professionals. In this paper, initial work on exploring edge-based segmentation algorithms to identify areas on an image that form the skin lesion is presented. Four edge-segmentation operators namely Canny, Prewitt, Sobel, and Roberts were tested using images from online image database. Experiments show results with mixed accuracy depending on the quality of image as well as the pattern of the skin lesions

ADiBA big data adoption framework: accelerating Big Data Revolution 5.0

Researchers have formulated the revolution of Big Data into several stages, from stage 1 using raw data until stage 5 using operational intelligence and advanced analytics is used to provide wisdom. However, for organisations to reap the values from big data adoption and implementation, they must embrace Big Data Revolution 5.0: digital acceleration. At this stage, Big Data Analytics (BDA) becomes an asset from which, businesses can get new insights and aid value creation, resulting in increased profits. BDA will play a large part in extending an organisation's presence, which will lead to enticing possible investors and hasten global growth. In this paper, we proposed a framework that aid organisations toward big data adoption and implementation that can create the best value for the organisations. It covers the whole value chain of big data adoption and implementation from the enculturation of big data in the organisation, to business understanding, to data management and governance, to big data project planning, to data understanding, to data preparation, to procurement, to analytics modeling, data product development, evaluation of model and data product deployment, maintenance, and upgrades and inculturation of data analytics into business. The framework has been successfully used in several Malaysian organisations, government, semi-government, and private sectors