Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA

In our age of big data and growing computational power, versatility in data analysis is important. This study presents a flexible way to combine statistics and machine learning for data analysis of a large-scale educational survey. The authors used statistical and machine learning methods to explore German students’ attitudes towards information and communication technology (ICT) in relation to mathematical and scientific literacy measured by the Programme for International Student Assessment (PISA) in 2015 and 2018. Implementations of the random forest (RF) algorithm were applied to impute missing data and to predict students’ proficiency levels in mathematics and science. Hierarchical linear models (HLM) were built to explore relationships between attitudes towards ICT and mathematical and scientific literacy with the focus on the nested structure of the data. ICT autonomy was an important variable in RF models, and associations between this attitude and literacy scores in HLM were significant and positive, while for other ICT attitudes the associations were negative (ICT in social interaction) or non-significant (ICT competence and ICT interest). The need for further research on ICT autonomy is discussed, and benefits of combining statistical and machine learning approaches are outlined

Using a Machine Learning Approach to Implement and Evaluate Product Line Features

Bike-sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility. In this paper we are interested in studying and modeling the behavior of features that permit the end user to access, with her/his web browser, the status of the Bike-Sharing system. In particular, we address features able to make a prediction on the system state. We propose to use a machine learning approach to analyze usage patterns and learn computational models of such features from logs of system usage. On the one hand, machine learning methodologies provide a powerful and general means to implement a wide choice of predictive features. On the other hand, trained machine learning models are provided with a measure of predictive performance that can be used as a metric to assess the cost-performance trade-off of the feature. This provides a principled way to assess the runtime behavior of different components before putting them into operation.Comment: In Proceedings WWV 2015, arXiv:1508.0338

To Cheat or not to Cheat? Sex Differences and Academic Performance as Factors of Cheating Behavior

Cheating behavior at higher education is a global phenomenon since it is found at any university in any country. This study is to examine whether sex differences and academic performance reflect the different likelihood of doing cheating among students. Using a questionnaire, data were collected from 436 students selected from different semesters and study programs in all faculties at a State Islamic University. Data were analyzed by using logistic regression, both separately and simultaneously. The results of data analysis revealed that male students tend to be more likely to do cheating categories than that of their female counterparts. It also found that academic performance affects negatively the likelihood of students to cheat in three categories of cheating behavior, but not in the other three. There is no stimulant effect of sex and academic performance on the likelihood of all categories of cheating behaviors. In other words, the effect of sex differences is not depended on academic performance and vice versa

To Cheat or not to Cheat? Sex Differences and Academic Performance as Factors of Cheating Behavior

Cheating behavior at higher education is a global phenomenon since it is found at any university in any country. This study is to examine whether sex differences and academic performance reflect the different likelihood of doing cheating among students. Using a questionnaire, data were collected from 436 students selected from different semesters and study programs in all faculties at a State Islamic University. Data were analyzed by using logistic regression, both separately and simultaneously. The results of data analysis revealed that male students tend to be more likely to do cheating categories than that of their female counterparts. It also found that academic performance affects negatively the likelihood of students to cheat in three categories of cheating behavior, but not in the other three. There is no stimulant effect of sex and academic performance on the likelihood of all categories of cheating behaviors. In other words, the effect of sex differences is not depended on academic performance and vice versa

Effects of Tacit Knowledge on the Performance of Selected Universities in Kenya

Tacit knowledge (TK) is non-codified and personal (sticky) knowledge that is difficult to transfer. TK cannot be said to be significant if there is a lack of tangible contributions. Universities can only realize such returns when there is growth in terms of (financial base, products, processes, customer base, employees’ loyalty) financial and non-financial indicators. The main objective of the research was to evaluate the effect of TK on organizational performance in selected universities in Kenya. The study adopted a mixed research approach as informed by pragmatism research paradigm. Data was collected from a study population of 65 respondents from four study sites which were Kibabii University, University of Nairobi, KCA University, and the University of Eastern Africa, Baraton. Semi-structured questionnaires were administered to academic deans; directors of research, innovation, and ICT; and heads of library services as well as planning and administration. Qualitative data was analyzed through conversation analysis, content analysis, and R which is a computer-assisted data analysis software. Chi-square tests, as well as multinomial logistic regression, were used for the quantitative data analysis. The findings of this study indicate that universities value TK as a key asset for organizational performance. The study identified TK as an asset that has helped institutions to grow in terms of work processes, decision making, and the creation of new products and/or services