Interpretable statistics for complex modelling: quantile and topological learning

As the complexity of our data increased exponentially in the last decades, so has our need for interpretable features. This thesis revolves around two paradigms to approach this quest for insights. In the first part we focus on parametric models, where the problem of interpretability can be seen as a “parametrization selection”. We introduce a quantile-centric parametrization and we show the advantages of our proposal in the context of regression, where it allows to bridge the gap between classical generalized linear (mixed) models and increasingly popular quantile methods. The second part of the thesis, concerned with topological learning, tackles the problem from a non-parametric perspective. As topology can be thought of as a way of characterizing data in terms of their connectivity structure, it allows to represent complex and possibly high dimensional through few features, such as the number of connected components, loops and voids. We illustrate how the emerging branch of statistics devoted to recovering topological structures in the data, Topological Data Analysis, can be exploited both for exploratory and inferential purposes with a special emphasis on kernels that preserve the topological information in the data. Finally, we show with an application how these two approaches can borrow strength from one another in the identification and description of brain activity through fMRI data from the ABIDE project

Constructive Use of Errors in Teaching the UML Class Diagram in an IS Engineering Course

A class diagram is one of the most important diagrams of Unified Modeling Language (UML) and can be used for modeling the static structure of a software system. Learning from errors is a teaching approach based on the assumption that errors can promote learning. We applied a constructive approach of using errors in designing a UML class diagram in order to (a) categorize the students’ errors when they design a class diagram from a text scenario that describes a specific organization and (b) determine whether the learning-from-errors approach enables students to produce more accurate and correct diagrams. The research was conducted with college students (N = 45) studying for their bachelor’s degree in engineering. The approach is presented, and the learning-from-errors activity is illustrated. We present the students’ errors in designing the class diagram before and after the activity, together with the students’ opinions about applying the new approach in their course. Twenty errors in fundamental components of the class diagram design were observed. The students erred less after the activity of learning from errors. The displayed results show the relevance and potential of embedding our approach in teaching. Furthermore, the students viewed the learning-from-errors activity favorably. Thus, one of the benefits of our developed activity is increased student motivation. In light of the improved performance of the task, and the students’ responses to the learning-from-errors approach, we recommend that information systems teachers use similar activities in different fields and on various topics

Crosscutting, what is and what is not? A Formal definition based on a Crosscutting Pattern

Crosscutting is usually described in terms of scattering and tangling. However, the distinction between these concepts is vague, which could lead to ambiguous statements. Sometimes, precise definitions are required, e.g. for the formal identification of crosscutting concerns. We propose a conceptual framework for formalizing these concepts based on a crosscutting pattern that shows the mapping between elements at two levels, e.g. concerns and representations of concerns. The definitions of the concepts are formalized in terms of linear algebra, and visualized with matrices and matrix operations. In this way, crosscutting can be clearly distinguished from scattering and tangling. Using linear algebra, we demonstrate that our definition generalizes other definitions of crosscutting as described by Masuhara & Kiczales [21] and Tonella and Ceccato [28]. The framework can be applied across several refinement levels assuring traceability of crosscutting concerns. Usability of the framework is illustrated by means of applying it to several areas such as change impact analysis, identification of crosscutting at early phases of software development and in the area of model driven software development

Software engineering whispers: The effect of textual vs. graphical software design descriptions on software design communication

Context:\ua0Software\ua0engineering\ua0is a social and collaborative activity. Communicating and sharing knowledge between\ua0software\ua0developers requires much effort. Hence, the quality of\ua0communication\ua0plays an important role in influencing project success. To better understand the\ua0effect\ua0of\ua0communication\ua0on project success, more in-depth empirical studies investigating this phenomenon are needed. Objective: We investigate the\ua0effect\ua0of using a\ua0graphical\ua0versus\ua0textual\ua0design\ua0description\ua0on co-located\ua0software\ua0design\ua0communication. Method: Therefore, we conducted a family of experiments involving a mix of 240\ua0software\ua0engineering\ua0students from four universities. We examined how different\ua0design\ua0representations (i.e.,\ua0graphical\ua0vs.\ua0textual) affect the ability to Explain, Understand, Recall, and Actively Communicate knowledge. Results: We found that the\ua0graphical\ua0design\ua0description\ua0is better than the\ua0textual\ua0in promoting Active Discussion between developers and improving the Recall of\ua0design\ua0details. Furthermore, compared to its unaltered version, a well-organized and motivated\ua0textual\ua0design\ua0description–that is used for the same amount of time–enhances the recall of\ua0design\ua0details and increases the amount of active discussions at the cost of reducing the perceived quality of explaining

The Direct and Indirect Paths Impacting Geometry Student Achievement

Previous studies have shown that several key variables influence student achievement in geometry, but more research needs to be conducted to determine how these variables interact. A model of achievement in geometry was tested on a sample of 102 high school students. Structural equation modeling was used to test hypothesized relationships among variables linked to successful problem solving in geometry. These variables, including motivation, achievement emotions, pictorial representation, and categorization skills were examined for their influence on geometry achievement. Results indicated that the model fit well. Achievement emotions, specifically boredom and enjoyment, had a significant influence on student motivation. Student motivation influenced students’ use of pictorial representations and achievement. Pictorial representation also directly influenced achievement. Categorization skills had a significant influence on pictorial representations and student achievement. The implications of these findings for geometry instruction and for future research are discussed

Think-pair-square learning: Improving student’s collaborative skills and cognitive learning outcome on animal diversity course

Empowering collaborative skills and optimizing learning outcomes are essential goals in every course. The aim of this study was to determine the effect of Think-Pair-Square (TPS) learning model on student collaborative skills and their cognitive learning outcomes. This study was Lesson-Study-based Classroom Action Research (CAR) carried out in two cycles. The subjects of this study consisted of 32 students who took Animal Diversity course. The CAR consisted of four phases i.e. planning, action, observation, and reflection. At the action phase, Lesson Study (LS) was conducted and consist of Plan, Do, and See. The instruments used were LS observation sheet, collaborative observation sheet, and cognitive test. The observation and test results of the both cycles were calculated and compared each other. There were improvements in the both student’s collaborative skills and cognitive learning outcome as high as 14% and 7.56, respectively. Therefore, TPS model can strengthen the student’s collaborative skills and cognitive learning outcome