Giftedness as property: Troubling whiteness, wealth, and gifted education in the US

The purposes of this article are to illumine the racist genealogy of gifted education policies and practices in the United States, to demonstrate how deficit discourses continue today, and to provide personal examples from the field of how educators can begin to question the status quo, resist taken-for-granted assumptions, and alternatively make substantive changes at the local level. I also aim to demonstrate how giftedness is an example of whiteness as property, or unearned white privilege, that, unintentionally or not, maintains a social caste system in school

Teachers\u27 Conceptions of Mathematics and Intelligent Tutoring System Use

The purpose of this mixed-methods study was to investigate the relationship between teachers’ conceptions of mathematics and their use of intelligent tutoring systems for mathematics instruction. Intelligent tutoring systems are adaptive computer programs which administer mathematics instruction to students based on their cognitive state. A conception is a mixture of beliefs and knowledge. The participants in this study were 93 junior high school mathematics teachers from three school districts in the Midwest. Data were gathered using a two-part online survey. The first part of the survey contained questions about their use of intelligent tutoring systems, graphing calculators, Desmos and dynamic geometry software. The second part of the survey contained Likert questions from the teachers’ version of the Conceptions of Mathematics Inventory. Desmos is a website providing interactive classroom activities and a user-friendly graphing calculator. Dynamic geometry software is a class of interactive geometry programs. The quantitative analysis revealed no statistically significant interactions between teachers’ conception scores and intelligent tutoring system use, or between teachers’ conception scores and how intelligent tutoring systems were used. There were statistically significant interactions between teachers’ conception scores and their use of graphing calculators, Desmos, and dynamic geometry software. The qualitative analysis revealed that teachers used intelligent tutoring systems for differentiation. Teachers used graphing calculators, Desmos, and dynamic geometry software for visual, computational, and exploratory purposes. Teachers exclusively using intelligent tutoring systems to incorporate technology should also incorporate technology which promotes student exploration

Revita: a Language-learning Platform at the Intersection of ITS and CALL

This paper presents Revita, a Web-based platform for language learning—beyond the beginner level. We anchor the presentation in a survey, where we review the literature about recent advances in the fields of computer-aided language learning (CALL) and intelligent tutoring systems (ITS). We outline the established desiderata of CALL and ITS and discuss how Revita addresses (the majority of) the theoretical requirements of CALL and ITS. Finally, we claim that, to the best of our knowledge, Revita is currently the only platform for learning/tutoring beyond the beginner level, that is functional, freely-available and supports multiple languages.Peer reviewe

Self build design and construction processes and the future of sustainable design education

The paper was given during the First Annual International Conference on Construction, 20-23 June2011,in Athens, Greece; it was submitted and peer reviewed after the conference again and selected to be included in a book as a chapter.Research groups' funds

Improving Adult Learners’ Experience with Continuing Professional Education: A Transformational Path to Andragogy

The topic of this organizational improvement plan is to improve the learning satisfaction of adult learners in professional programs who return to universities to attend formal academic courses or programmes in class while pursuing their employment. Its purpose is to promote and support the teaching faculty involved in such courses and programmes in adopting andragogical methods to support those adult learners and improve their learning experience. The research is informed organizational leadership, organizational change, and by andragogic theories. This study will impact adult learners, teaching faculty, administrators, and organizational culture as it relates to Continuing Professional Education (CPE), with particular focus on helping instructors teach adults better using more andragogical methods. It is expected that in doing so, they will improve their learning experience, academic achievement, recruiting and retention of the growing number of professional adult learners seeking formal education at universities. The design of this study is based on quality assurance and quality improvement. It is a research-informed quality improvement plan based on the trans-theoretical model (Prochaska & Velicer, 1997), the intelligent leadership model (Sydänmaanlakka, 2003), and transformational leadership (Burns, 1978; Bass, 1985). This study promotes an organizational culture of learning that meets the targeted needs of learners, to improve their learning experience, while bringing greater awareness of the needs of adult learning

Sequencing in Intelligent Tutoring Systems based on online learning Recommenders

In dieser Arbeit entwickeln und testen wir Algorithmen für Learning Analytics, die die personalisierte Sequenzierung von Matheaufgaben erlauben. Die Sequenzierung schlägt die nächste Aufgabe einem Schüler vor, die seine Lernbedürfnisse entspricht. Unsere Lösung basiert auf Vygotskys „Zone of Proximal Development“ (ZPD), das die weder zu einfachen noch zu schwierigen Aufgaben für den Schüler bestimmt. Der Sequenzer, auch Vygotsky Policy Sequencer genannt, ist in der Lage Aufgaben im ZPD zu erkennen, dank die von einem Vorhersagealgorithmus geschätzte zukünftige Leistung des Schülers. Die Arbeit enthält folgende Beiträge: (1) Die Evaluation der Anwendbarkeit von Matrix Factorization als Inhaltsdomäne unabhängige Algorithmus für die Vorhersage der Leistung der Schüler. (2) Anpassung und Evaluation eines Matrix Factorization basierenden Algorithmus, der die zeitliche Evolution der Schülerkenntnisse einbezieht. (3) Entwicklung von zwei Ansätzen für die Aktualisierung von Matrix Factorization basierenden Modellen durch den Kalman Filter. Zwei Aktualisierungsfunktionen sind benutzt: (a) eine einfache, die nur die letzte vom Schüler gesehene Aufgabe betrachtet, und (b) eine, die in der Lage ist, seine fehlenden Kompetenzen einzuschätzen. (4) Ein neues Verfahren von Machine Learning gesteuerte Sequenzer zu testen durch die Modellierung einer simulierten Umgebung, die aus simulierte Schülern und Aufgaben mit stetigen erzielten und gebrauchten Fähigkeiten und Schwierigkeitsgraden besteht. (5) Die Entwicklung einer minimal eingreifenden API für die leichte Integration von Machine Learning basierende Komponente in größere Systeme, um das Integrationsrisiko und die Kosten vom Know-How-Transfer zu minimieren. Dank all diesen Beiträgen, wurde der VPS in ein großes kommerzielles System integriert und mit 100 Kinder für einen Monat getestet. Der VPS zeigte Lerneffekte und wahrgenommene Erlebnisse, die mit den von den ITS Sequenzer vergleichbar sind. Infolge der besseren VPS Modellierfähigkeiten konnten die Schüler beendeten die Aufgaben schneller lösen.In this thesis we design and test Learning Analytics algorithms for personalized tasks' sequencing that suggests the next task to a student according to his/her specific needs. Our solution is based on a sequencing policy derived from the Vygotsky's Zone of Proximal Development (ZPD), which denes those tasks that are neither too easy not too dicult for the student. The sequencer, called Vygotsky Policy Sequencer (VPS), can identify tasks in the ZPD thanks to the information it receives from performance prediction algorithms able to estimate the knowledge of the student. Under this context we describe hereafter the thesis contributions. (1) A feasibility evaluation of domain independent Matrix Factorization applied in ITS for Performance Prediction. (2) An adaption and the related evaluation of a domain independent update for online learning Matrix Factorization in ITS. (3) A novel Matrix Factorization update method based on Kalman Filters approach. Two different updating functions are used: (a) a simple one considering the task just seen, and (b) one able to derive the skills' deficiency of the student. (4) A new method for offline testing of machine learning controlled sequencers by modeling simulated environment composed by a simulated students and tasks with continuous knowledge and score representation and different diffculty levels. (5) The design of a minimal invasive API for the lightweight integration of machine learning components in larger systems to minimize the risk of integration and the cost of expertise transfer. Profiting from all these contributions, the VPS was integrated in a commercial system and evaluated with 100 children over a month. The VPS showed comparable learning gains and perceived experience results with those of the ITS sequencer. Finally, thanks to its better modeling abilities, the students finish faster the assigned tasks

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings