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Using Multivariate Statistics

Data Mining Practical Machine Learning Tools and Techniques

A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

Multimedia in Learning

What Video Games Have to Teach Us about Learning and Literacy

Este libro describe un importante conjunto de habilidades clave que aumentan el aprendizaje mas profundo, la preparacion para la universidad y la carrera, el aprendizaje centrado en el estudiante y el pensamiento de orden superior. Estas etiquetas incluyen habilidades cognitivas y no cognitivas, como pensamiento critico, resolucion de problemas, colaboracion, comunicacion efectiva, motivacion, persistencia y aprender a aprender. Las habilidades del siglo XXI tambien incluyen creatividad, innovacion y etica que son importantes para el exito posterior y pueden desarrollarse en entornos de aprendizaje formales o informales. Este informe tambien describe como estas habilidades se relacionan entre si y con las habilidades y contenidos academicos mas tradicionales en las disciplinas clave de lectura, matematicas y ciencias. Educacion para la vida y el trabajo: Desarrollar conocimientos y habilidades transferibles en el siglo XXI resume los hallazgos de la investigacion que investiga la importancia de tales habilidades para el exito en la educacion, el trabajo y otras areas de responsabilidad adulta y que demuestra la importancia de desarrollar estas habilidades. en la educacion K-16. En este informe, se identifican caracteristicas relacionadas con el aprendizaje de estas habilidades, que incluyen desarrollo profesional docente, curriculo, evaluacion, programas extraescolares y extraescolares y centros de aprendizaje informal como exhibiciones y museos.

Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century

Prior research has established that peer tutors can benefit academically from their tutoring experiences. However, although tutor learning has been observed across diverse settings, the magnitude of these gains is often underwhelming. In this review, the authors consider how analyses of tutors’ actual behaviors may help to account for variation in learning outcomes and how typical tutoring behaviors may create or undermine opportunities for learning. The authors examine two tutoring activities that are commonly hypothesized to support tutor learning: explaining and questioning. These activities are hypothesized to support peer tutors’ learning via reflective knowledge-building, which includes self-monitoring of comprehension, integration of new and prior knowledge, and elaboration and construction of knowledge. The review supports these hypotheses but also finds that peer tutors tend to exhibit a pervasive knowledge-telling bias. Peer tutors, even when trained, focus more on delivering knowledge rather th...

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Understanding Tutor Learning: Knowledge-Building and Knowledge-Telling in Peer Tutors’ Explanations and Questions:

Students engaged in learning a large body of related knowledge often possess some incorrect naive knowledge about the domain. These “misconceptions” must be removed and/or the correct conception must be built in order for students to achieve a deep understanding. This repair process is generally referred to as “conceptual change.” However, although conceptual change has been discussed for several decades within different research contexts, the literature nevertheless presents a somewhat blurry picture of what exactly misconceptions are, what constitutes conceptual change, and why conceptual change is difficult. In this chapter, we suggest that one should think of misconceptions as ontological miscategorizations of concepts. From this perspective, conceptual change can be viewed as a simple shift of a concept across lateral (as opposed to hierarchical) categories. We argue that this process is difficult if students lack awareness of when a shift is necessary and/or lack an alternative category to shift into. These ideas are explored using a detailed example (i.e. diffusion) from abroad class of science concepts (i.e. emergent processes) that are often robustly misunderstood by students.

The processes and challenges of conceptual change

Previous research on peer tutoring has found that students sometimes benefit academically from tutoring other students. In this study we combined quantitative and qualitative analyses to explore how untrained peer tutors learned via explaining and responding to tutee questions in a non-reciprocal tutoring setting. In support of our hypotheses, we found that tutors learned most effectively when their instructional activities incorporated reflective knowledge-building in which they monitored their own understanding, generated inferences to repair misunderstandings, and elaborated upon the source materials. However, tutors seemed to adopt a knowledge-telling bias in which they primarily summarized the source materials with little elaboration. Tutors’ reflective knowledge-building activities, when they occurred, were more frequently elicited by interactions with their tutee. In particular, when tutees asked questions that contained an inference or required an inferential answer, tutors’ responses were more likely to be elaborative and metacognitive. Directions for future research are also discussed.

Tutor learning: The role of explaining and responding to questions.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1551-6709.2011.01207.x

Misconceived causal explanations for emergent processes.

We have developed a learning environment where students teach a computer agent using visual representations, and can monitor the agent's learning progress by asking her questions and having her take quizzes. The system provides self-regulated learning and metacognitive support via dialog-embedded prompts from Betty, the teachable agent, and Mr. Davis, the mentor agent. Our primary goals have been to support learning of complex science topics in middle school classrooms and facilitate development of metacognitive skills to support future learning. In this paper, we discuss methods that we have employed for detecting and characterizing students' behavior patterns from their activity sequences on the system. In particular, we discuss a method for learning hidden Markov models (HMM) from the activity logs. We demonstrate that the HMM structure corresponds to students' aggregated behavior patterns in the learning environment. Overall, the HMM technique allows us to go beyond simple frequency and sequence analyses, such as individual activity and pre-defined pattern counts, instead using exploratory methods to examine how these activities cohere in larger patterns over time. The paper outlines a study conducted in a 5th grade science classroom, presents the models derived from the students' activity sequences, interprets the model structure as aggregate patterns of their learning behaviors, and links these patterns to students' use of self-regulated learning strategies. The results illustrate that those who teach an agent demonstrate better learning performance and better use of metacognitive monitoring behaviors than students who only learn for themselves. We also observed more advanced and focused monitoring behaviors in the students who received metacognitive strategy feedback from the mentor agent while they taught the teachable agent.

/pdf/measuring-self-regulated-learning-skills-through-social-2l1cyzxm1g.pdf

Rod D. Roscoe

Papers

Understanding Tutor Learning: Knowledge-Building and Knowledge-Telling in Peer Tutors’ Explanations and Questions:

The processes and challenges of conceptual change

Tutor learning: The role of explaining and responding to questions.

Misconceived causal explanations for emergent processes.

Measuring self-regulated learning skills through social interactions in a teachable agent environment