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One of the most important requirements for successful learning experiences is learning activity on a regular basis. The problem with today's learning system is that the learners often get stuck while using traditional learning systems because they can't motivate them to fast learning and make a creative mind. Successful learning requires getting knowledge on regular bases and keeping it memorable as long as possible. The problem with traditional learning methods is that the learner's mind glued in its state and it does not provide any motivation to them to get new knowledge and improve their skills. Microlearning provides a new teaching paradigm which can allow knowledge and information to divided into small chunks and deliver it to the learners. Microlearning can make the learning subjects easy to understand and memorable for a longer period. In this work, we tested microlearning teaching methods for ICT subject in the Primary school. We chose two groups from a Primary school in Sulaimani city. Then we teach the class using microlearning methods in one of them and traditional methods in the other for six weeks. After testing both groups getting the results, Microlearning group showed around 18% better learning than traditional group. We can conclude that using microlearning techniques, the effectiveness, and efficiency of learning can be improved. Also, the knowledge can stay memorable for longer periods.

Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. SDP is essentially studied during few last decades as it leads to assure the quality of software systems. The quick forecast of defective or imperfect artifacts in software development may serve the development team to use the existing assets competently and more effectively to provide extraordinary software products in the given or narrow time. Previously, several canvassers have industrialized models for defect prediction utilizing machine learning (ML) and statistical techniques. ML methods are considered as an operative and operational approach to pinpoint the defective modules, in which moving parts through mining concealed patterns amid software metrics (attributes). ML techniques are also utilized by several researchers on healthcare datasets. This study utilizes different ML techniques software defect prediction using seven broadly used datasets. The ML techniques include the multilayer perceptron (MLP), support vector machine (SVM), decision tree (J48), radial basis function (RBF), random forest (RF), hidden Markov model (HMM), credal decision tree (CDT), K-nearest neighbor (KNN), average one dependency estimator (A1DE), and Naïve Bayes (NB). The performance of each technique is evaluated using different measures, for instance, relative absolute error (RAE), mean absolute error (MAE), root mean squared error (RMSE), root relative squared error (RRSE), recall, and accuracy. The inclusive outcome shows the best performance of RF with 88.32% average accuracy and 2.96 rank value, second-best performance is achieved by SVM with 87.99% average accuracy and 3.83 rank values. Moreover, CDT also shows 87.88% average accuracy and 3.62 rank values, placed on the third position. The comprehensive outcomes of research can be utilized as a reference point for new research in the SDP domain, and therefore, any assertion concerning the enhancement in prediction over any new technique or model can be benchmarked and proved.

Purpose This study aimed to examine the impact of ICT and digital knowledge on students’ thoughts and beliefs. Using Information and Communication Technology (ICT) in learning and teaching processes can improve the interpretation of knowledge, not only in the learning process but also for thoughts and beliefs. Beliefs and thoughts as propositional content are understood to be a subjective manner of knowing and becoming a focal point of education process. In addition, ICT plays a vital role in enhancing the efficiency of the teaching process which can change the thoughts of learners. So, in this paper, the usage of ICT in education was considered as a key factor for improving students’ thoughts and beliefs. In addition, a conceptual model was proposed to evaluate this impact. Design/methodology/approach Data were collected from 384 students from secondary schools in Iran. For assessing the elements of the model, a complete questionnaire was designed. For statistical analysis of questionnaires, SPSS 22 and SMART-PLS 3.2 software package was used. Findings The obtained results showed the high strength of the proposed model. The outcomes indicated that digital technology acceptance positively affects students’ thoughts and beliefs. In addition, the findings showed that the role of digital knowledge, digital training facilities and digital education content on students’ thoughts and beliefs was significant. Research limitations/implications The authors deal with one experiment and so the results cannot be generalized. The trail should be repeated with many groups and in diverse contexts. Originality/value Despite the importance of the investigating the impact of ICT and digital knowledge on the students’ thoughts and beliefs, the relationship among these factors was not examined well in previous research. Thus, the investigation of the impact of ICT and digital knowledge on the students’ thoughts and beliefs is the main originality of this research. For this goal, a new conceptual model is proposed, which has 11 sub-indicators within four variables: digital technology acceptance, digital knowledge, digital training facilities and digital education content.

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