Enhancement of Predicting Students Performance Model Using Ensemble Approaches and Educational Data Mining Techniques

Student performance prediction is extremely important in today’s educational system. Predicting student achievement in advance can assist students and teachers in keeping track of the student’s progress. Today, several institutes have implemented a manual ongoing evaluation method. Students benefit from such methods since they help them improve their performance. In this study, we can use educational data mining (EDM), which we recommend as an ensemble classifier to anticipate the understudy accomplishment forecast model based on data mining techniques as classification techniques. This model uses distinct datasets which represent the student’s intercommunication with the instructive model. The exhibition of an understudy’s prescient model is evaluated by a kind of classifiers, for instance, logistic regression, naïve Bayes tree, artificial neural network, support vector system, decision tree, random forest, and k -nearest neighbor. Additionally, we used set processes to evolve the presentation of these classifiers. We utilized Boosting, Random Forest, Bagging, and Voting Algorithms, which are the normal group of techniques used in studies. By using ensemble methods, we will have a good result that demonstrates the dependability of the proposed model. For better productivity, the various classifiers are gathered and, afterward, added to the ensemble method using the Vote procedure. The implementation results demonstrate that the bagging method accomplished a cleared enhancement with the DT model, where the DT algorithm accuracy with bagging increased from 90.4% to 91.4%. Recall results improved from 0.904 to 0.914. Precision results also increased from 0.905 to 0.915.

The School Attachment Monitor—A novel computational tool for assessment of attachment in middle childhood

Background Attachment research has been limited by the lack of quick and easy measures. We report development and validation of the School Attachment Monitor (SAM), a novel measure for largescale assessment of attachment in children aged 5–9, in the general population. SAM offers automatic presentation, on computer, of story-stems based on the Manchester Child Attachment Story Task (MCAST), without the need for trained administrators. SAM is delivered by novel software which interacts with child participants, starting with warm-up activities to familiarise them with the task. Children’s story completion is video recorded and augmented by ‘smart dolls’ that the child can hold and manipulate, with movement sensors for data collection. The design of SAM was informed by children of users’ age range to establish their task understanding and incorporate their innovative ideas for improving SAM software. Methods 130 5–9 year old children were recruited from mainstream primary schools. In Phase 1, sixty-one children completed both SAM and MCAST. Inter-rater reliability and rating concordance was compared between SAM and MCAST. In Phase 2, a further 44 children completed SAM complete and, including those children completing SAM in Phase 1 (total n = 105), a machine learning algorithm was developed using a “majority vote” procedure where, for each child, 500 non-overlapping video frames contribute to the decision. Results Using manual rating, SAM-MCAST concordance was excellent (89% secure versus insecure; 97% organised versus disorganised; 86% four-way). Comparison of human ratings of SAM versus the machine learning algorithm showed over 80% concordance. Conclusions We have developed a new tool for measuring attachment at the population level, which has good reliability compared to a validated attachment measure and has the potential for automatic rating–opening the door to measurement of attachment in large populations.

The School Attachment Monitor - a novel computational tool for assessment of attachment in middle childhood

BackgroundAttachment research has been limited by the lack of quick and easy measures. We report development and validation of the School Attachment Monitor (SAM), a novel measure for largescale assessment of attachment in children aged 5-9, in the general population. SAM offers automatic presentation, on computer, of story-stems based on the Manchester Child Attachment Story Task (MCAST), without the need for trained administrators. SAM is delivered by novel software which interacts with child participants, starting with warm-up activities to familiarise them with the task. Children’s story completion is video recorded and augmented by ‘smart dolls’ that the child can hold and manipulate, with movement sensors for data collection. The design of SAM was informed by children of users’ age range to establish their task understanding and incorporate their innovative ideas for improving SAM software.Methods130 5-9 year old children were recruited from mainstream primary schools. In Phase 1, sixty-one children completed both SAM and MCAST. Inter-rater reliability and rating concordance was compared between SAM and MCAST. In Phase 2, a further 44 children completed SAM complete and, including those children completing SAM in Phase 1 (total n=105), a machine learning algorithm was developed using a “majority vote” procedure where, for each child, 500 non-overlapping video frames contribute to the decision.ResultsUsing manual rating, SAM-MCAST concordance was excellent (89% secure versus insecure; 97% organised versus disorganised; 86% four-way). Comparison of human ratings of SAM versus the machine learning algorithm showed over 80% concordance.ConclusionsWe have developed a new tool for measuring attachment at the population level, which has good reliability compared to a gold-standard attachment measure and has the potential for automatic rating – opening the door to measurement of attachment in large populations.

Leader Selection and Service Delivery in Community Groups: Experimental Evidence from Uganda

In developing countries, NGOs and governments often rely on local groups for the delivery of financial and public services. This paper studies how the design of rules used for group leader selection affects leader identity and shapes service delivery. To do so, we randomly assign newly formed savings and loan groups to select their leaders using either a public discussion procedure or a private vote procedure. Leaders selected with a private vote are found to be less positively selected on socioeconomic characteristics. This results in groups that are more inclusive toward poor members, without being less economically efficient. (JEL D72, O16, O17, O22, Z13)

The Vote of Confidence in Parliamentary Democracies

I present a formal model of the confidence vote procedure, an institutional arrangement that permits a prime minister to attach the fate of a particular policy to a vote on government survival. The analysis indicates that confidence vote procedures make it possible for prime ministers to exercise significant control over the nature of policy outcomes, even when these procedures are not actually invoked. Neither cabinet ministers, through their authority over specific portfolios, nor members of parliament, through the use of no-confidence motions, can counteract the prime minister's policy control on the floor of parliament. The analysis also illuminates the circumstances under which prime ministers should invoke confidence vote procedures, focusing attention on the position-taking incentives of the parties that support the government, rather than on the level of policy conflict between the government and parliament.