21 research outputs found
The conventional versus a constructionist Scratch programming and first-year students' achievements in higher education classes: experimental data.
No full textGlobally, learning or teaching the first programming (popularly called CS1) remains a significant educational challenge. Indicators such as CS1 students' engagement, failure and attrition rates, and lack of diversity, continue to show the need for innovating the learning or teaching of novice computer science students. To ease initiating novices to programming, Scratch, a visual programming language, has become a staple of K-12 CS1 classes.
As outcomes of a research project aiming to explore a constructionist Scratch pedagogy with novice CS students in higher education, we present these datasets. In the research lasting two successive academic sessions, we conducted two quasi-experimental studies involving four intact CS1 classes in selected public polytechnic in the north central Nigeria. In each study, we randomly assigned the classes to the experimental and control groups, constituting the constructionist Scratch and the conventional CS1 classes, respectively.
Instruments for collecting data include a student profile questionnaire, a pretest, and posttest. Sequel to ethical clearance and permission from the selected schools, we conducted each study during the first semester of each academic session, in the first seven to eight weeks. During the first to second week, we administered students who consented to take part with the questionnaire and the pretest. Learning or teaching in the two classes lasted six weeks. Then both classes took the posttest. An independent CS educator who is not part of this research marked all the achievement tests, following a rubric prepared by the first author. To strengthen the research design and the possibility of arriving at valid causal evidence, we employed a Coarsened Exact Matching (CEM) algorithm to generate matched samples of experimental and control data, which we used in the analysis.
Data presented here includes the raw, unmatched and matched experimental datasets from both studies. A researcher can make use of the data:
To explore if some background variables not addressed in the original research may moderate CS1 students' achievements. For instance, their prior achievements in mathematics, physics, or English.
To uncover some interesting patterns using machine learning algorithms.
To validate the outcome of the original experiment by using the unmatched, matched or newly generated matched samples.
The authors welcome further research collaborations in using the data or the accompanying research instruments.
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Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms
No full textExploring the dataset features through the application of clustering algorithms is a viable means by which the conceptual description of such data can be revealed for better understanding, grouping and decision making. Some clustering algorithms, especially those that are partitioned-based, clusters any data presented to them even if similar features do not present. This study explores the performance accuracies of partitioning-based algorithms and probabilistic model-based algorithm. Experiments were conducted using k-means, k-medoids and EM-algorithm. The study implements each algorithm using RapidMiner Software and the results generated was validated for correctness in accordance to the concept of external criteria method. The clusters formed revealed the capability and drawbacks of each algorithm on the data points
Microarray cancer feature selection: Review, challenges and research directions
No full textMicroarray technology has become an emerging trend in the domain of genetic research in which many researchers employ to study and investigate the levels of genes’ expression in a given organism. Microarray experiments have lots of application areas in the health sector such as diseases prediction and diagnosis, cancer study and soon. The enormous quantity of raw gene expression data usually results in analytical and computational complexities which include feature selection and classification of the datasets into the correct class or group. To achieve satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is critical and sensitive in the classification task. Therefore, this paper presents a comprehensive survey of studies on microarray cancer classification with a focus on feature selection methods. In this paper, the taxonomy of the various feature selection methods used for microarray cancer classification and open research issues have been extensively discussed
Development of an inventory management system using association rule
No full textStores today still make use of manual approaches to keeping inventory which could be cumbersome. Having a computerized inventory system would make inventory management more efficient and effective. In this chapter, an Inventory Management System using Association Rule was developed which will ensure proper record keeping and keep items in stocks updated. ANGULARJS, a JavaScript framework, was used for the implementation of the system, PHP (hypertext pre-processor) was used for the backend of the system development as well as the database management, HTML was used alongside CSS for the system interface design and NoSQL database was the database used for this research. In conclusion, a computerized inventory system that had been improved using the association rule method was the resulting product useful for creating transactions, updating items in stock, record keeping, generating reports for decision making, and lastly, the system will make the stores more effective
An Empirical Investigation of the Prevalence of Osteoarthritis in South West Nigeria: A Population-Based Study
No full textToday, Osteoarthritis remains the most prevalent chronic joint disease and a potentially incapacitating joint illness. It is an enduring health problem which cannot be cure though it can be managed. Osteoarthritis remains a serious public health problem because its burden is high, people who live with it have a greater risk of developing anxiety / or depression and if it is not properly managed, it can bring about disability as well as impairing quality of life. This paper presents a statistical correlation between the reported risk factors of Osteoarthritis and its prevalence in Nigeria. Statistical tests were performed to investigate if there is enough evidence for inferring that the risk factors for Osteoarthritis are true for the whole of Nigerian populatio
Ogundokun Roseline Oluwaseun, Adebiyi Marion Oluwabunmi, Abikoye Oluwakemi C., Oladele Tinuke O.,Dataset on the academic performance of students in 12 programmes from a private university
No full textThe dataset on the academic performance of students in 12 programs from a private university. The overall people sampled for the observation is 2490 undergraduates excavated from 12 programs which are as follows Computer Science (CIS), Mathematics (MAT), Electrical and Electronics Engineering (EEE), Biochemistry (BCH), Mechanical Engineering (MCE), Microbiology (MCB), Civil Engineering (CVE), Computer Engineering (CEN), Chemical Engineering (CHE), Industrial Chemistry (CHM), Information and Communication (ICE), Petroleum Engineering (PET).THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV
Application of Data Mining Algorithms for Feature Selection and Prediction of Diabetic Retinopathy
No full textDiabetes Retinopathy is a disease which results from a prolonged
case of diabetes mellitus and it is the most common cause of loss of vision in
man. Data mining algorithms are used in medical and computer fields to find
effective ways of forecasting a particular disease. This research was aimed at
determining the effect of using feature selection in predicting Diabetes
Retinopathy. The dataset used for this study was gotten from diabetes retinopathy
Debrecen dataset from the University of California in a form suitable for mining.
Feature selection was executed on diabetes retinopathy data then the Imple�mentation of k-Nearest Neighbour, C4.5 decision tree, Multi-layer Perceptron
(MLP) and Support Vector Machines was conducted on diabetes retinopathy data
with and without feature selection. There was access to the algorithms in terms of
accuracy and sensitivity. It is observed from the results that, making use of
feature selection on algorithms increases the accuracy as well as the sensitivity of
the algorithms considered and it is mostly reflected in the support vector machine
algorithm. Making use of feature selection for classification also increases the
time taken for the prediction of diabetes retinopathy
Inhibition and Compressive-Strength Performance of Na₂Cr₂O₇ and C₁₀H₁₄N₂Na₂O₈.2H₂O in Steel-Reinforced Concrete in Corrosive Environments
No full textThis paper studied corrosion-inhibition and compressive-strength performances of NaThe accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
An investigative and evaluative study of factors affecting quality of agricultural and farm information services in Kerala
No full textAgriculture is not only a country’s backbone of food, livelihood and ecological security systems, but is also the very soul of its sovereignty. In Kerala population density is high and land is scarce. To achieve sustainable advancement in quality of human life, meeting the domestic food requirement is to be given foremost priority in development plans. As the area of cultivation cannot be increased and growth of population cannot be controlled growth in food production is to be achieved by qualitative improvement in farming. This requires improvements in material inputs, farming techniques, storage technology and research. Effective integration of these factors is tied closely to adequate information flow, which can be ensured only by an efficient information system for agricultural education, research, extension and development. So evaluation and improvement of existing information services is very crucial for sustainable agricultural growth. The study evaluates the existing information resources, facilities, services, possibilities for resource sharing, accessibility of external sources, and the factors that affect the quality and efficiency of information services in agricultural sector. Coverage is limited to the State of Kerala. Sample consist 105 institutions of different levels, and information users consisting of 426 scientists and 220 farmers. Different sets of questionnaires and interview schedule were used to elicit information. The study found that agricultural research conducted at various institutions in the region at huge public expense has generated knowledge for improving production. Along with these huge collections of acquired content is also stored in the sector. But when a farmer, an extension worker, a scientist or an administrator needs information it is not easily accessible. The study found that agricultural sector fails to effectively bank on information resources available due to the lack of an information system and network. Recommends an Agricultural and Farm Information System for Kerala. Suggests a model plan for a computer communication network for resource sharing between the agricultural institutions in the State, which will also ensure, smooth flow of results of research down to the grassroots level to achieve maximum productivity in agriculture
Malicious Uniform Resource Locator Detection Using Wolf Optimization Algorithm and Random Forest Classifier
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