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Learning Malware Analysis
Malware analysis and memory forensics are powerful analysis and investigation techniques used in reverse engineering, digital forensics, and incident response. This book teaches you the concepts, tools, and techniques to determine the behavior and characteristics of malware using malware analysis and memory forensics.
eBook
Guide to Advanced Software Testing
This richly illustrated reference takes you on a complete tour of the multi-dimensional software testing universe. The book offers you a practical understanding of essential software testing topics and their relationships and interdependencies. This unique resource provides a thorough overview of software testing and its purpose and value. It covers topics ranging from handling failures, faults, and mistakes, to the cost of fault corrections, “scoping” the test effort, using standards to guide testing. You find numerous examples and exercises in each chapter to help ensure your complete understanding of the material.
eBook
Design and Implementation of a Learning Analytics Toolkit for Teachers
Learning Analytics can provide powerful tools for teachers in order to support them in the iterative process of improving the effectiveness of their courses and to collaterally enhance their students' performance. In this paper, we present the theoretical background, design, implementation, and evaluation details of eLAT, a Learning Analytics Toolkit, which enables teachers to explore and correlate learning object usage, user properties, user behavior, as well as assessment results based on graphical indicators. The primary aim of the development of eLAT is to process large data sets in microseconds with regard to individual data analysis interests of teachers and data privacy issues, in order to help them to self-reflect on their technology-enhanced teaching and learning scenarios and to identify opportunities for interventions and improvements.
Journal Article
Machine learning applications in software engineering
Machine learning deals with the issue of how to build computer programs that improve their performance at some tasks through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This book deals with the subject of machine learning applications in software engineering. It provides an overview of machine learning, summarizes the state-of-the-practice in this niche area, gives a classification of the existing work, and offers some application guidelines. Also included in the book is a collection of previously published papers in this research area.
eBook
Computer-supported argumentation: A review of the state of the art
Argumentation is an important skill to learn. It is valuable not only in many professional contexts, such as the law, science, politics, and business, but also in everyday life. However, not many people are good arguers. In response to this, researchers and practitioners over the past 15–20 years have developed software tools both to support and teach argumentation. Some of these tools are used in individual fashion, to present students with the “rules” of argumentation in a particular domain and give them an opportunity to practice, while other tools are used in collaborative fashion, to facilitate communication and argumentation between multiple, and perhaps distant, participants. In this paper, we review the extensive literature on argumentation systems, both individual and collaborative, and both supportive and educational, with an eye toward particular aspects of the past work. More specifically, we review the types of argument representations that have been used, the various types of interaction design and ontologies that have been employed, and the system architecture issues that have been addressed. In addition, we discuss intelligent and automated features that have been imbued in past systems, such as automatically analyzing the quality of arguments and providing intelligent feedback to support and/or tutor argumentation. We also discuss a variety of empirical studies that have been done with argumentation systems, including, among other aspects, studies that have evaluated the effect of argument diagrams (e.g., textual versus graphical), different representations, and adaptive feedback on learning argumentation. Finally, we conclude by summarizing the “lessons learned” from this large and impressive body of work, particularly focusing on lessons for the CSCL research community and its ongoing efforts to develop computer-mediated collaborative argumentation systems.
Journal Article
The assessment of the usability of digital educational resources: An interdisciplinary analysis from two systematic reviews
International reports analyzing current and future educational trends with an emphasis on technologies applied to education declare the importance of the design and application of digital educational resources. Guaranteeing its usability allows obtaining an adequate resource with a high pedagogical and technological quality. The objective of this paper is to analyze the empirical researches to determine if exists convergence between educational and computational researches on the assessment of the usability of digital educational resources. To fulfill the objective, the PRISMA protocol was used to carry out two systematic reviews and answer the two scientific questions. The results show that in few cases an adequate integration is achieved between: (1) the criteria for assessing usability as established by Software Engineering; (2) the methods and computational models to assess usability and, (3) the criteria established in pedagogical usability. Due to these shortcomings, a model for evaluating the usability of digital educational resources is proposed as future work. It concludes with the importance of interdisciplinary integration to assess the usability of digital educational resources.
Journal Article
Empowering personalized learning with an interactive e-book learning system for elementary school students
Today various types of tablet computers are used, including iPad and Android Tablets. These individual portable digital devices can be used as e-book readers to support mobile personalized learning. Though many studies have investigated e-books by targeting undergraduate students, yet less attention has been paid to children. Therefore, an Interactive E-book Learning System (IELS) was developed for elementary school students in this study. The interactive principles in multimedia learning were adopted to empower students' personalized learning experiences with e-books. Meanwhile, in order to meet student's learning needs, a group of in-service teachers were consulted for acquiring their domain expertise. Personalized learning functions, such as e-annotation and bookmarks, content searching, and learning process tracking, were designed to reinforce student learning. Two investigations were conducted for evaluation of the developed system. The first investigation was conducted with 166 elementary school students to evaluate the usability and functionality of the developed system and the feedback from the students. It was found that the usability and functionality of the developed system were well-suited for most of the students. The second investigation was to evaluate the learning effect of the developed system. The results showed that using an e-book or printed book made no significant difference to the students' reading accuracy; however, the learning process tracking technique of IELS can provide detailed logs about the actual learning processes which can be used by the system to provide further assistance to individual learner. This study concludes that such a tailor-made e-book learning system could achieve a better personalized learning experience for elementary school students.
Journal Article