Detecting students who are conducting inquiry Without Thinking Fastidiously (WTF) in the Context of Microworld Learning Environments

In recent years, there has been increased interest and research on identifying the various ways that students can deviate from expected or desired patterns while using educational software. This includes research on gaming the system, player transformation, haphazard inquiry, and failure to use key features of the learning system. Detection of these sorts of behaviors has helped researchers to better understand these behaviors, thus allowing software designers to develop interventions that can remediate them and/or reduce their negative impacts on student learning. This work addresses two types of student disengagement: carelessness and a behavior we term WTF (“Without Thinking Fastidiously”) behavior. Carelessness is defined as not demonstrating a skill despite knowing it; we measured carelessness using a machine learned model. In WTF behavior, the student is interacting with the software, but their actions appear to have no relationship to the intended learning task. We discuss the detector development process, validate the detectors with human labels of the behavior, and discuss implications for understanding how and why students conduct inquiry without thinking fastidiously while learning in science inquiry microworlds. Following this work we explore the relationship between student learner characteristics and the aforementioned disengaged behaviors carelessness and WTF. Our goal was to develop a deeper understanding of which learner characteristics correlate to carelessness or WTF behavior. Our work examines three alternative methods for predicting carelessness and WTF behaviors from learner characteristics: simple correlations, k-means clustering, and decision tree rule learners

Self-Directed Learning

This book on self-directed learning (SDL) is devoted to original academic scholarship within the field of education, and is the 6th volume in the North-West University (NWU) SDL book series. In this book the authors explore how self-directed learning can be considered an imperative for education in a complex modern society. Although each chapter represents independent research in the field of self-directed learning, the chapters form a coherent contribution concerning the scholarship of self-directed learning, and specifically the effect of environmental and praxis contexts on the enhancement of self-directed learning in a complex society. The publication as a whole provides diverse perspectives on the importance of self-directed learning in varied contexts. Scholars working in a wide range of fields are drawn together in this scholarly work to present a comprehensive dialogue regarding self-directed learning and how this concept functions in a complex and dynamic higher education context. This book presents a combination of theory and practice, which reflects selected conceptual dimensions of self-directed learning in society, as well as research-based findings pertaining to current topical issues relating to implementing self-directed learning in the modern world. The varied methodologies provide the reader with different and balanced perspectives, as well as varied and innovative ideas on how to conduct research in the field of self-directed learning

Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors [discussant]

This symposium addresses how different classes of research methods, all based upon the use of log data from educational software, can facilitate the analysis of students’ learning strategies and behaviors. To this end, four multi-method programs of research are discussed, including the use of qualitative, quantitative-statistical, quantitative-modeling, and educational data mining methods. The symposium presents evidence regarding the applicability of each type of method to research questions of different grain sizes, and provides several examples of how these methods can be used in concert to facilitate our understanding of learning processes, learning strategies, and behaviors related to motivation, meta-cognition, and engagement

Self-Directed Learning

This book on self-directed learning (SDL) is devoted to original academic scholarship within the field of education, and is the 6th volume in the North-West University (NWU) SDL book series. In this book the authors explore how self-directed learning can be considered an imperative for education in a complex modern society. Although each chapter represents independent research in the field of self-directed learning, the chapters form a coherent contribution concerning the scholarship of self-directed learning, and specifically the effect of environmental and praxis contexts on the enhancement of self-directed learning in a complex society. The publication as a whole provides diverse perspectives on the importance of self-directed learning in varied contexts. Scholars working in a wide range of fields are drawn together in this scholarly work to present a comprehensive dialogue regarding self-directed learning and how this concept functions in a complex and dynamic higher education context. This book presents a combination of theory and practice, which reflects selected conceptual dimensions of self-directed learning in society, as well as research-based findings pertaining to current topical issues relating to implementing self-directed learning in the modern world. The varied methodologies provide the reader with different and balanced perspectives, as well as varied and innovative ideas on how to conduct research in the field of self-directed learning

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

New measurement paradigms

This collection of New Measurement Paradigms papers represents a snapshot of the variety of measurement methods in use at the time of writing across several projects funded by the National Science Foundation (US) through its REESE and DR K–12 programs. All of the projects are developing and testing intelligent learning environments that seek to carefully measure and promote student learning, and the purpose of this collection of papers is to describe and illustrate the use of several measurement methods employed to achieve this. The papers are deliberately short because they are designed to introduce the methods in use and not to be a textbook chapter on each method. The New Measurement Paradigms collection is designed to serve as a reference point for researchers who are working in projects that are creating e-learning environments in which there is a need to make judgments about students’ levels of knowledge and skills, or for those interested in this but who have not yet delved into these methods