Authoring Example-based Tutors for Procedural Tasks

Researchers who have worked on authoring systems for intelligent tutoring systems (ITSs) have examined how examples may form the basis for authoring. In this chapter, we describe several such systems, consider their commonalities and differences, and reflect on the merit of such an approach. It is not surprising perhaps that several tutor developers have explored how examples can be used in the authoring process. In a broader context, educators and researchers have long known the power of examples in learning new material. Students can gather much information by poring over a worked example, applying what they learn to novel problems. Often these worked examples prove more powerful than direct instruction in the domain. For example, Reed and Bolstad (1991) found that students learning solely by worked examples exhibited much greater learning than those learning instruction based on procedures. By extension then, since tutor authoring can be considered to be teaching a tabula rasa tutor, tutor authoring by use of examples may be as powerful as directly programming the instruction, while being easier to do

Supporting Collaborative Learning in Computer-Enhanced Environments

As computers have expanded into almost every aspect of our lives, the ever-present graphical user interface (GUI) has begun facing its limitations. Demanding its own share of attention, GUIs move some of the users\u27 focus away from the task, particularly when the task is 3D in nature or requires collaboration. Researchers are therefore exploring other means of human-computer interaction. Individually, some of these new techniques show promise, but it is the combination of multiple approaches into larger systems that will allow us to more fully replicate our natural behavior within a computing environment. As computers become more capable of understanding our varied natural behavior (speech, gesture, etc.), the less we need to adjust our behavior to conform to computers\u27 requirements. Such capabilities are particularly useful where children are involved, and make using computers in education all the more appealing. Herein are described two approaches and implementations of educational computer systems that work not by user manipulation of virtual objects, but rather, by user manipulation of physical objects within their environment. These systems demonstrate how new technologies can promote collaborative learning among students, thereby enhancing both the students\u27 knowledge and their ability to work together to achieve even greater learning. With these systems, the horizon of computer-facilitated collaborative learning has been expanded. Included among this expansion is identification of issues for general and special education students, and applications in a variety of domains, which have been suggested

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

A Foundation For Educational Research at Scale: Evolution and Application

The complexities of how people learn have plagued researchers for centuries. A range of experimental and non-experimental methodologies have been used to isolate and implement positive interventions for students\u27 cognitive, meta-cognitive, behavioral, and socio-emotional successes in learning. But the face of learning is changing in the digital age. The value of accrued knowledge, popular throughout the industrial age, is being overpowered by the value of curiosity and the ability to ask critical questions. Most students can access the largest free collection of human knowledge (and cat videos) with ease using their phones or laptops and omnipresent cellular and Wi-Fi networks. Viewing this new-age capacity for connection as an opportunity, educational stakeholders have delegated many traditional learning tasks to online environments. With this influx of online learning, student errors can be corrected with immediacy, student data is more prevalent and actionable, and teachers can intervene with efficiency and efficacy. As such, endeavors in educational data mining, learning analytics, and authentic educational research at scale have grown popular in recent years; fields afforded by the luxuries of technology and driven by the age-old goal of understanding how people learn. This dissertation explores the evolution and application of ASSISTments Research, an approach to authentic educational research at scale that leverages ASSISTments, a popular online learning platform, to better understand how people learn. Part I details the evolution and advocacy of two tools that form the research arm of ASSISTments: the ASSISTments TestBed and the Assessment of Learning Infrastructure (ALI). An NSF funded Data Infrastructure Building Blocks grant (#1724889, $494,644 2017-2020), outlines goals for the new age of ASSISTments Research as a result of lessons learned in recent years. Part II details a personal application of these research tools with a focus on the framework of Self Determination Theory. The primary facets of this theory, thought to positively affect learning and intrinsic motivation, are investigated in depth through randomized controlled trials targeting Autonomy, Belonging, and Competence. Finally, a synthesis chapter highlights important connections between Parts I & II, offering lessons learned regarding ASSISTments Research and suggesting additional guidance for its future development, while broadly defining contributions to the Learning Sciences community

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research