Contribution: A model of tiered in-lab, compulsory lab programming sessions suitable for use in flipped learning environments. This article also addresses the scarcity of research that explores the effects of in-class activity organization. Background: When facing a subject they perceive as dry or difficult, some students may feel discouraged from making an effort to learn. Previous research has shown that students have a positive attitude toward assignment tiering, as a form of scaffolding, in voluntary, take-home assignments. Related models either are designed for different settings or have obvious problems with applicability in flipped learning environments. Intended Outcomes: A lab environment that motivates students to learn and consequently to perform better on in-class evaluations and on exams. The approach should be positively received by students. Application Design: Drawing upon the previous teaching experience, the proposed approach is a modification of an assignment tiering model found in the academic literature. With two gated tiered assignments per class, the steepness of the learning curve is alleviated for struggling students, while the injection of deliberate errors, reflexive questions, and ad hoc modification requests keep experienced students engaged with challenging tasks. Findings: The results of the six-year study, performed on an assembly language programming subject, are encouraging: Test groups were better in terms of motivation and performance, and the great majority of students expressed a positive attitude toward the new model.

Many research centers and medical institutions have been accumulating a vast amount of various biological and chemical data over the past decade and this trend continues. Based on Linked Data vision, many semantic applications for distributed access

This paper presents a comparison of the efficacy of unsupervised and supervised discretization methods for educational data from blended learning environment. Naïve Bayes classifier was trained for each discretized data set and...