Data quality assessment outcomes are essential to ensure useful analytical processes results. Relevant computational approaches provide assessment support, especially to data defects that present more precise rules. However, data defects that are more dependent of data context knowledge challenge the data quality assessment since the process involves human supervision. Visualization systems belong to a class of supervised tools that can make visible data defect structures. Despite their considerable design knowledge encodings, there is little support design to visual quality assessment of data defects. Therefore, this work reports a case study that has explored which and how visualization properties facilitate visual detection of data defect. Its outcomes offer a first set of implications to design visualization system to permit data quality visual assessment.

The generation of individualized exams can contribute to a more reliable assessment of the students. Manually performing this procedure may not be feasible, even more on a large scale. An alternative to deal with it is the automatic generation of questions. This paper discusses an innovative solution to simplify test generation and correction through parameterized questions in the context of a four-month Introduction to Programming course under a blended- learning (IP-BL) approach. It combines the open-source tool MCTest with Moodle and VPL plugin to generate and also automatically evaluate parameterized programming language questions. We applied an intervention based on this solution in two IP-BL groups (a total of 171 enrolled students) using Java.

This work presents our first experience in applying a Problem-Based Learning approach to improving students’ abilities on how to analyze and evaluate database performance problems. The preliminary qualitative results reveal our approach contribution to students learning process. Additionally, we also discuss key lessons learned and indicate some future areas of improvement.

Teaching Digital Imaging Processing (DIP) is challenging, primarily because of its mathematical and algorithms complexities. Despite the recent growth in the field, comprehensive resources are lacking to support DIP education. To address this gap, this paper introduces a practical course utilizing a Python library named morph.py designed for beginners and accessible on Google Colab. This interactive course employs illustrative examples and hands-on exercises to facilitate the learning of fundamental DIP concepts and operators. It begins with basic concepts (e.g., image representation) and progresses to more advanced topics, including image transformations and feature extraction. We conducted an exploratory case study in one group (N=15) and gathered their perception through a voluntary survey. Our quantitative analysis strongly supports our teaching method's effectiveness based on the morph.py library, which addresses the difficulties of teaching DIP to beginners.