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

Do Metacognitive Strategies Improve Student Achievement in Secondary Science Classrooms?

Increasing prevalence of high-stakes testing calls for focus on value-added teaching and learning practices. Following is an inquiry regarding metacognitive teaching and learning practices as it pertains to secondary science classrooms. Research shows that the orchestration and inclusion of metacognitive strategies in the science classroom improve achievement under the following preconditions: (1) are pervasively embedded in the educational structure; (2) are part of appropriately rigorous and relevant curriculum; (3) are supported by ‘metacognitive friendly’ teaching strategies; (4) are explicitly practiced by students and teachers; and (5) enable students to take responsibility for their own learning

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

Support of the collaborative inquiry learning process: influence of support on task and team regulation

Regulation of the learning process is an important condition for efficient and effective learning. In collaborative learning, students have to regulate their collaborative activities (team regulation) next to the regulation of their own learning process focused on the task at hand (task regulation). In this study, we investigate how support of collaborative inquiry learning can influence the use of regulative activities of students. Furthermore, we explore the possible relations between task regulation, team regulation and learning results. This study involves tenth-grade students who worked in pairs in a collaborative inquiry learning environment that was based on a computer simulation, Collisions, developed in the program SimQuest. Students of the same team worked on two different computers and communicated through chat. Chat logs of students from three different conditions are compared. Students in the first condition did not receive any support at all (Control condition). In the second condition, students received an instruction in effective communication, the RIDE rules (RIDE condition). In the third condition, students were, in addition to receiving the RIDE rules instruction, supported by the Collaborative Hypothesis Tool (CHT), which helped the students with formulating hypotheses together (CHT condition). The results show that students overall used more team regulation than task regulation. In the RIDE condition and the CHT condition, students regulated their team activities most often. Moreover, in the CHT condition the regulation of team activities was positively related to the learning results. We can conclude that different measures of support can enhance the use of team regulative activities, which in turn can lead to better learning results

Developing personalized education. A dynamic framework

Personalized education—the systematic adaptation of instruction to individual learners—has been a long-striven goal. We review research on personalized education that has been conducted in the laboratory, in the classroom, and in digital learning environments. Across all learning environments, we find that personalization is most successful when relevant learner characteristics are measured repeatedly during the learning process and when these data are used to adapt instruction in a systematic way. Building on these observations, we propose a novel, dynamic framework of personalization that conceptualizes learners as dynamic entities that change during and in interaction with the instructional process. As these dynamics manifest on different timescales, so do the opportunities for instructional adaptations—ranging from setting appropriate learning goals at the macroscale to reacting to affective-motivational fluctuations at the microscale. We argue that instructional design needs to take these dynamics into account in order to adapt to a specific learner at a specific point in time. Finally, we provide some examples of successful, dynamic adaptations and discuss future directions that arise from a dynamic conceptualization of personalization. (DIPF/Orig.

Semiotics and Symbiosis – “Gap-Closing”: How Signs, Symbols, and Structure Impact the Teaching and Learning of Mathematics for Middle School African American Students

The purpose of this theory-building project was to generate a scientific platform through which society might stop using the data from standardized mathematics assessments to evaluate and scrutinize students and instead evaluate, scrutinize, and improve the processes and activities through which students engage in mathematics learning (Hilliard, 1994: Kozol, 2005; Ladson-Billings, 1997; Martin, 2000; Steele, 1992). In particular, this project focused on the syntax, semantics, and pragmatics (Peirce, 1902; Saussure, 1908/1998) of mathematics situations and the activities through which students might leverage these tools to construct their own mathematics knowledge in an effort to achieve mathematics proficiency (Kilpatrick, Swafford, & Findell, 2001). The participants of this project were self-identified African American male and female middle school students located in the southeastern region of the Unite States. This theory-building project used a re-engineered teaching experiment methodology (Steffe, 1991) located within a sociocultural and radical constructivist ideological frame (von Glasersfeld, 1983; Vygotsky, 1930/1978). More specifically, the students were mentored through a cycle of exploration, introduction, application, and inquiry when given mathematical situations. Data from observations and Socratic inquiries were collected and analyzed using cultural-historical activity theory (CHAT; Vygotsky, 1930/1978) and a newly developed coding protocol in order to seek aspects of metacognition, cognition, and mathematics proficiency (Saldaña, 2016). The reporting and analysis of the data revealed that the students could demonstrate progressive acts in their pursuit of mathematics proficiency. How the students were able to make such achievements were to be found, in part, in how they understood the semiotic aspects of any given mathematical situation––its syntax, semantics, and problem-solving elements. In addition, the students gave deeper and intentional attention to the metacognitive knowledge and metacognitive skills necessary to emphasize these semiotic aspects (Veenman & Spaan, 2005). Consistently, the responses from and the observations of each student were unique representations of their experiential selves. In the end, the aim of this theory-building project was to capture these unique representations and determine the specifics that might serve as components of a preliminary mathematics learning model

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

The effects of a multistrategy reading comprehension intervention on the reading skills of university athletes with reading deficits

A large number of entering college and university students are unable to derive meaning from print at age-expected levels. The purpose of this study was to determine the effectiveness of Collaborative Strategic Reading (CSR; Klingner, Vaughn, Dimino, Schumm, & Bryant, 2001) in improving the reading comprehension skills of underprepared college students. Sixteen (8 experimental and 8 control) first-time male college student athletes entering their freshman year at a research-intensive university in the southeastern United States participated in the study. An experimental design was implemented to address the following research question: What effects does a multistrategy reading comprehension intervention (i.e., CSR) have on the reading comprehension skills of academically underprepared students entering a postsecondary setting? Results showed there were statistically significant findings in favor of the experimental group for an informal dependent measure and non-significant results for a standardized measure. Study implications, limitations, and areas of future research are discussed