Intelligent Support for Computer Science Education presents the authors’ research journey into the effectiveness of human tutoring, with the goal of developing educational technology that can be used to improve introductory Computer Science education at the undergraduate level. Nowadays, Computer Science education is central to the concerns of society, as attested by the penetration of information technology in all aspects of our lives; consequently, in the last few years interest in Computer Science at all levels of schooling, especially at the college level, has been flourishing. However, introductory concepts in Computer Science such as data structures and recursion are difficult for novices to grasp. Key Features: Includes a comprehensive and succinct overview of the Computer Science education landscape at all levels of education. Provides in-depth analysis of one-on-one human tutoring dialogues in introductory Computer Science at college level. Describes a scalable, plug-in based Intelligent Tutoring System architecture, portable to different topics and pedagogical strategies. Presents systematic, controlled evaluation of different versions of the system in ecologically valid settings (18 actual classes and their laboratory sessions). Provides a time-series analysis of student behavior when interacting with the system. This book will be of special interest to the Computer Science education community, specifically instructors of introductory courses at the college level, and Advanced Placement (AP) courses at the high school level. Additionally, all the authors’ work is relevant to the Educational Technology community, especially to those working in Intelligent Tutoring Systems, their interfaces, and Educational Data Mining, in particular as applied to human-human pedagogical interactions and to user interaction with educational software.

Intelligent Support for Computer Science Education presents the authors’ research journey into the effectiveness of human tutoring, with the goal of developing educational technology that can be used to improve introductory Computer Science education at the undergraduate level. Nowadays, Computer Science education is central to the concerns of society, as attested by the penetration of information technology in all aspects of our lives; consequently, in the last few years interest in Computer Science at all levels of schooling, especially at the college level, has been flourishing. However, introductory concepts in Computer Science such as data structures and recursion are difficult for novices to grasp. Key Features: Includes a comprehensive and succinct overview of the Computer Science education landscape at all levels of education. Provides in-depth analysis of one-on-one human tutoring dialogues in introductory Computer Science at college level. Describes a scalable, plug-in based Intelligent Tutoring System architecture, portable to different topics and pedagogical strategies. Presents systematic, controlled evaluation of different versions of the system in ecologically valid settings (18 actual classes and their laboratory sessions). Provides a time-series analysis of student behavior when interacting with the system. This book will be of special interest to the Computer Science education community, specifically instructors of introductory courses at the college level, and Advanced Placement (AP) courses at the high school level. Additionally, all the authors’ work is relevant to the Educational Technology community, especially to those working in Intelligent Tutoring Systems, their interfaces, and Educational Data Mining, in particular as applied to human-human pedagogical interactions and to user interaction with educational software.

This monograph presents the challenges, vision and context to design smart learning objects (SLOs) through Computer Science (CS) education modelling and feature model transformations. It presents the latest research on the meta-programming-based generative learning objects (the latter with advanced features are treated as SLOs) and the use of educational robots in teaching CS topics. The introduced methodology includes the overall processes to develop SLO and smart educational environment (SEE) and integrates both into the real education setting to provide teaching in CS using constructivist and project-based approaches along with evaluation of pedagogic outcomes. Smart Learning Objects for Smart Education in Computer Science will appeal to researchers in CS education particularly those interested in using robots in teaching, course designers and educational software and tools developers. With research and exercise questions at the end of each chapter students studying CS related courses will find this work informative and valuable too.

At the centre of the methodology used in this book is STEM learning variability space that includes STEM pedagogical variability, learners’ social variability, technological variability, CS content variability and interaction variability. To design smart components, firstly, the STEM learning variability space is defined for each component separately, and then model-driven approaches are applied. The theoretical basis includes feature-based modelling and model transformations at the top specification level and heterogeneous meta-programming techniques at the implementation level. Practice includes multiple case studies oriented for solving the task prototypes, taken from the real world, by educational robots. These case studies illustrate the process of gaining interdisciplinary knowledge pieces identified as S-knowledge, T-knowledge, E-knowledge, M-knowledge or integrated STEM knowledge and evaluate smart components from the pedagogical and technological perspectives based on data gathered from one real teaching setting. Smart STEM-Driven Computer Science Education: Theory, Methodology and Robot-based Practices outlines the overall capabilities of the proposed approach and also points out the drawbacks from the viewpoint of different actors, i.e. researchers, designers, teachers and learners.

Developing educational software requires thinking, problematizing, representing, modeling, implementing and analyzing pedagogical objectives and issues, as well as conceptual models and software architectures. Computer scientists face the difficulty of understanding the particular issues and phenomena to be taken into account in educational software projects and of avoiding a naïve technocentered perspective. On the other hand, actors with backgrounds in human or social sciences face the difficulty of understanding software design and implementation issues, and how computer scientists engage in these tasks. Tchounikine argues that these difficulties cannot be solved by building a kind of “general theory” or “general engineering methodology” to be adopted by all actors for all projects: educational software projects may correspond to very different realities, and may be conducted within very different perspectives and with very different matters of concern. Thus the issue of understanding each others’ perspectives and elaborating some common ground is to be considered in context, within the considered project or perspective. To this end, he provides the reader with a framework and means for actively taking into account the relationships between pedagogical settings and software, and for working together in a multidisciplinary way to develop educational software. His book is for actors engaged in research or development projects which require inventing, designing, adapting, implementing or analyzing educational software. The core audience is Master’s and PhD students, researchers and engineers from computer science or human and social sciences (e.g., education, psychology, pedagogy, philosophy, communications or sociology) interested in the issues raised by educational software design and analysis and in the variety of perspectives that may be adopted.

"This book reviews computational models and technologies for distance education, focusing on systems, infrastructures, and frameworks for delivering quality education"--Provided by publisher.

Building Intelligent Interactive Tutors discusses educational systems that assess a student's knowledge and are adaptive to a student's learning needs. The impact of computers has not been generally felt in education due to lack of hardware, teacher training, and sophisticated software. and because current instructional software is neither truly responsive to student needs nor flexible enough to emulate teaching. Dr. Woolf taps into 20 years of research on intelligent tutors to bring designers and developers a broad range of issues and methods that produce the best intelligent learning environments possible, whether for classroom or life-long learning. The book describes multidisciplinary approaches to using computers for teaching, reports on research, development, and real-world experiences, and discusses intelligent tutors, web-based learning systems, adaptive learning systems, intelligent agents and intelligent multimedia. It is recommended for professionals, graduate students, and others in computer science and educational technology who are developing online tutoring systems to support e-learning, and who want to build intelligence into the system. - Combines both theory and practice to offer most in-depth and up-to-date treatment of intelligent tutoring systems available - Presents powerful drivers of virtual teaching systems, including cognitive science, artificial intelligence, and the Internet - Features algorithmic material that enables programmers and researchers to design building components and intelligent systems