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Using Multivariate Statistics

Data Mining Practical Machine Learning Tools and Techniques

It is amazing to me how in all the hoopla and debate these days about the decline of education in the US we ignore the most fundamental of its causes. Our students have changed radically. Today’s students are no longer the people our educational system was designed to teach. Today’s students have not just changed incrementally from those of the past, nor simply changed their slang, clothes, body adornments, or styles, as has happened between generations previously. A really big discontinuity has taken place. One might even call it a “singularity” – an event which changes things so fundamentally that there is absolutely no going back. This so-called “singularity” is the arrival and rapid dissemination of digital technology in the last decades of the 20 th century.

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Digital Natives, Digital Immigrants

How to Do Things With Words

Recent studies of eye movements in reading and other information processing tasks, such as music reading, typing, visual search, and scene perception, are reviewed. The major emphasis of the review is on reading as a specific example of cognitive processing. Basic topics discussed with respect to reading are (a) the characteristics of eye movements, (b) the perceptual span, (c) integration of information across saccades, (d) eye movement control, and (e) individual differences (including dyslexia). Similar topics are discussed with respect to the other tasks examined. The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined. Theoretical and practical considerations concerning the use of eye movement data are also discussed.

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Eye movements in reading and information processing: 20 years of research.

Assessment of collaborative problem solving.

Alexander, Schallert, and Reynolds (2009/this issue) proposed a definition and landscape of learning that included 9 principles and 4 dimensions (what, who, where, when). This commentary reflects on the utility of this definition and 4-dimensional landscape from the standpoint of educational psychologists who have a cognitive science perspective. Their analysis has practical value in positioning different research programs in the landscape, planning research road maps, and identifying the scope of research efforts. However, it is argued that the learning definition is underspecified and that the learning landscape is both cumbersome and insufficiently constrained. Cognitive scientists are more likely to be inspired by theories and testable principles that have more teeth.

Cognitive Scientists Prefer Theories and Testable Principles With Teeth

: While human tutoring and mentoring are common teaching tools, current US Army standards for training and education are group instruction and classroom training, also known as one-to-many instruction. Recently, the US Army has placed significant emphasis on self-regulated learning (SRL) methods to augment institutional training where Soldiers will be largely responsible for managing their own learning. In support of the US Army Learning Model and to provide affordable, tailored SRL training and educational capabilities for the US Army, the US Army Research Laboratory is investigating and developing adaptive tools and methods to largely automate the authoring (creation), delivery of instruction, and evaluation of computer-regulated training and education capabilities. A major goal within this research program is to reduce the time and skill required to author, deliver, and evaluate adaptive technologies to make them usable by a larger segment of the training and educational community. This research includes 6 interdependent research vectors: individual learner and unit modeling, instructional management principles, domain modeling, authoring tools and methods, and evaluation tools and methods. This report (one of 6 interdependent research outlines) focuses on effectiveness evaluation research for adaptive training and education with the goal of determining the individual, training, and organizational characteristics that influence the adaptive tutoring process before, during, and after training.

Effectiveness Evaluation Tools and Methods for Adaptive Training and Education in Support of the US Army Learning Model: Research Outline

Recent research has indicated that learning environments that intentionally induce confusion to promote deep inquiry can be beneficial for learning if students engage in confusion resolution processes and if relevant scaffolds are provided. However, it is unlikely that these environments will benefit all students, so it is necessary to identify the student profiles that most benefit from confusion induction. We investigated how individual differences (e.g., prior knowledge, interest, attributional complexity) impacted confusion and learning outcomes in an environment that induced confusion via false system feedback (e.g., negative feedback after a correct response). A k-means cluster analysis revealed four clusters that varied on cognitive ability and cognitive drive. We found that students in the high cognitive ability + high cognitive drive cluster reported more confusion after receiving false feedback compared to the other clusters. These students also performed better on tasks requiring knowledge transfer, but only when they were meaningfully confused.

Who Benefits from Confusion Induction during Learning? An Individual Differences Cluster Analysis

Self-explanations (SE) are an effective method to promote learning because they can help students identify gaps and inconsistencies in their knowledge and revise their faulty mental models Given this potential, it is beneficial for intelligent tutoring systems (ITS) to promote SEs and adaptively respond based on SE quality We developed and evaluated classification models using combinations of SE content (eg, inverse weighted word-overlap) and contextual cues (eg, SE response time, topic being discussed) SEs were coded based on correctness and presence of different types of errors We achieved some success at classifying SE quality using SE content and context For correct vs incorrect discrimination, context-based features were more effective, whereas content-based features were more effective when classifying different types of errors Implications for automatic assessment of learner SEs by ITSs are discussed

Arthur C. Graesser

Papers

Assessment of collaborative problem solving.

Cognitive Scientists Prefer Theories and Testable Principles With Teeth

Effectiveness Evaluation Tools and Methods for Adaptive Training and Education in Support of the US Army Learning Model: Research Outline

Who Benefits from Confusion Induction during Learning? An Individual Differences Cluster Analysis

Automatic evaluation of learner self-explanations and erroneous responses for dialogue-based ITSs