This study examined the potential role of big data in chemistry education research. Big data refer to data sets that are too large or complex for traditional data processing application software to adequately deal with. With large amounts of information streaming in from countless sources, chemistry education researchers are faced with finding new and innovative ways to manage big data. Chemistry education researchers armed with data-driven insight can make a significant impact on school systems, students and curriculums. By analyzing big data, they can identify at-risk students, make sure students are making adequate progress and can implement a better system for evaluation and support of chemistry teachers and school administrators. With big data, chemistry education researchers and educators can identify root causes of chemistry student’s failures issues and defects in near-real time and detecting fraudulent behaviour among chemistry students among others.

This study was carried out to evaluate the extent to which School Access Programme (SAP) assisted teachers to acquire ICT skills in South-East Nigeria. The population of the study was 4,141 respondents. Direct observation was used to observe, monitor and document the ICT skills acquired by 40 teachers observed in 2 rural and 2 urban schools sampled for the direct observation of the study. The scores generated from the observation was analyzed with mean, standard deviation and student t-test. The result showed that SAP assisted teachers to acquire ICT skills in South-East Nigeria at great extent. Therefore, the federal government should establish more ICT centres to enable all the teachers in South-East Nigeria to acquire ICT skills. The programme should be evaluated in other zones in Nigeria to determine the skills acquired by teachers in those zones.

The major purpose of the study was to evaluate the extent school access programme assisted students the to acquire ICT skills in South-East Nigeria one reference question was answered and one hypothesis was tested at 0.05 level of significance. The study employed an evaluation design of the study was 10,000 students in South-East Nigeria in 2017/2018 session. Direct observation was used to observe, monitor 50 students in 2 rural and 2 urban schools to determine the ICT-skills they acquired with the assistance of SAP as an ICT platform in South-East Nigeria. The scores realized from the observation were analyzed with mean, standard deviation and student t-test. The result showed that the school access programme assisted students in South-East Nigeria to acquire ICT skills at very great extent.

On a daily basis, a huge quantity of data is generated in different areas of man’s end eavour. The rate of data generation has become unprecedented with the current advancements in information communication technology. It has been found that these huge data deposits are rich mines of information, thus, the quest to mine and harvest this information from large data sets. This opened up a very relevant research field known as data mining. It is concerned with intelligently seeking patterns and subtle trends in large data sets. When the data set mined comes from the educational sector, this is specially referred to as Educational Data Mining (EDM). In this research, we tried to explore the various applications of data mining technology in education and particularly mathematics education in the bid to underscore its relevance in mathematics education and its possible implication for innovation of mathematics education curriculum. This we did by first looking at data mining generally and then its evolution in the education sector as Educational Data Mining (EDM). Then specifically, we sought to provide evidence of the relevance of EDM to mathematics education by showing how EDM techniques can be applied to solve common problems associated with mathematics education and the implication of EDM techniques for effective curriculum innovation. It is then recommended that EDM be adopted and used more widely in different fields of education, especially, mathematics education as it has all it takes to move the system to the next level, especially, in taking managerial decisions, academic decisions and curriculum development decisions.

The purpose of this study is to examine statistical data analysis in mass communication research. There are three major methods of analysing data in mass communication research, namely: content analysis, discourse analysis and qualitative/computational analysis. None of these methods is unique or exclusive to mass communication as they are interdisciplinary. However, research activities in the discipline of mass communication in the 21st century are bereft of discourse analysis. Guided by the principles of object-oriented database theory, we argued that the procedures for data analysis in mass communication research are not unique to analysis procedures in other academic disciplines. Considering the observations made in the course of discussions and opinions reached, this study offers some recommendations as proposed solutions.

The purpose of this study was to investigate the effects of cooperative learning and analogy methods on secondary school student’s achievement and interest in chemistry. The study which adopted a quasiexperimental design was conducted in four secondary schools in Okene Local Government Area of Kogi State, Nigeria. Out of these schools, two schools were exposed to the use of cooperative learning (experimental 1) while the remaining two used analogy method (experimental 2). One experiment was meant to control the other. The sample comprised 253 Senior Secondary School 2 (SSS2) chemistry students. Mean and standard deviation were used to answer the research questions while Analysis of Covariance (ANCOVA) was used in testing the hypothesis at p<0.05. The result of the study revealed that both cooperative learning and analogy methods brought about significant shift in achievement and interest of SSS2 chemistry students.

Personalization is an essential concept for introducing efficient and interactive learning via. the internet. Personalization contributes to the improvement of children’s cognitive and language skills. As well as, it contributes to the speed of learning and make children more innovative. Adaptive e-Learning frameworks works to achieve these results accurately. Adaptation plays a distinctive role in providing educational material to suit age, types and learned degree of children. In this study, an original framework for developing an adaptive and intelligent e-Learning system for children has been proposed. Simplicity, ease of use and changeability are 3 keys considerations in the design of this framework. Online courses for children may be designed according to this framework as a means of enhancing their learning process. Furthermore, machine learning techniques are adopted across this research in order to incorporate intelligent concepts while adapting child-orientated online courses. Ultimately, machine learning techniques can extract useful information from text pattern, then these techniques are adopted to effectively classify contents and users in the proposed framework.

The International Software Benchmarking Standards Group (ISBSG) maintains a software development repository with 6,006 software projects. The definition of productivity is a single ratio of output to input and then combined with various cost factors leading to a single value. Of these values we have dataset makes it possible to calculate the productivity of projects, effort, size and quality. By contrast, the concept of performance is more comprehensive than productivity. This study explores a comparison between performance and productivity and how it can affect projects by several other factors that affect its using ISBSG dataset V.12. In this research, tree data analysis techniques were applied: data clustering, neural network. SPSS was used to conduct statistical analysis and data visualization.