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Using Multivariate Statistics

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Preface Chapter 1 The challenge Chapter 2 The nature of the evidence: A synthesis of meta-analyses Chapter 3 The argument: Visible teaching and visible learning Chapter 4: The contributions from the student Chapter 5 The contributions from the home Chapter 6 The contributions from the school Chapter 7 The contributions from the teacher Chapter 8 The contributions from the curricula Chapter 9 The contributions from teaching approaches - I Chapter 10 The contributions from teaching approaches - II Chapter 11: Bringing it all together Appendix A: The 800 meta-analyses Appendix B: The meta-analyses by rank order References

Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement

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

This article reviews the corpus of research on feedback, with a focus on formative feedback—defined as information communicated to the learner that is intended to modify his or her thinking or behavior to improve learning According to researchers, formative feedback should be nonevaluative, supportive, timely, and specific Formative feedback is usually presented as information to a learner in response to some action on the learner’s part It comes in a variety of types (eg, verification of response accuracy, explanation of the correct answer, hints, worked examples) and can be administered at various times during the learning process (eg, immediately following an answer, after some time has elapsed) Finally, several variables have been shown to interact with formative feedback’s success at promoting learning (eg, individual characteristics of the learner and aspects of the task) All of these issues are discussed This review concludes with guidelines for generating formative feedback

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Focus on Formative Feedback

The authors examined the effectiveness of self-regulated learning (SRL) training in facilitating college students' learning with hypermedia. Undergraduate students (N = 131) were randomly assigned to either a training condition or a control condition and used a hypermedia environment to learn about the circulatory system. Students in the SRL group were given a 30-min training session on the use of specific, empirically based SRL variables designed to foster their conceptual understanding; control students received no training. Pretest, posttest, and verbal protocol data were collected from both groups. The SRL condition facilitated the shift in learners' mental models significantly more than did the control condition; verbal protocol data indicated that this was associated with the use of the SRL variables taught during training.

Does training on self-regulated learning facilitate students' learning with hypermedia?

Research shows that learners of all ages have difficulties deploying key cognitive and metacognitive self-regulatory skills during learning about complex and challenging topics when using open-ended learning environments such as hypermedia. This article provides an overview of the research my students and I have conducted on how the use of self-regulated learning can foster and enhance students' learning about complex science topics using hypermedia. In this article, the term metacognitive tool is used deliberately to highlight (a) the role of metacognitive and self-regulatory processes used by learners during learning and (b) the role of computer environments in prompting, supporting, and modeling students' self-regulatory processes during learning in specific learning contexts (see Azevedo, 2005). I provide an overview of research regarding the use of hypermedia to learn about complex science topics and learning more generally, illustrate how self-regulated learning can be used as a guiding theoretical ...

Using Hypermedia as a Metacognitive Tool for Enhancing Student Learning? The Role of Self-Regulated Learning

A significant proportion of American high school students struggle with reading comprehension Theoretical models of reading comprehension might help researchers understand these difficulties, because they can point to variables that make the largest contributions to comprehension On the basis of an extensive review of the literature, we created a new model of reading comprehension, the direct and inferential mediation (DIME) model The model hypothesizes relationships among background knowledge, inferences, reading comprehension strategies, vocabulary, and word reading and addresses the direct and mediated effects of these 5 predictors on comprehension The authors tested the fit of the model and 3 variations of the model to data from 175 students in 9th grade The DIME model explained 66% of the variance in comprehension Vocabulary and background knowledge made the largest contributions to comprehension, followed by inference, word reading, and strategies Analyses of participants scoring below the 30th percentile on comprehension showed these students to have low scores on all of the measures The authors suggest that vocabulary and background knowledge interventions might be the best way to begin improving the academic reading comprehension of students like those in the sample

Testing and refining the direct and inferential mediation model of reading comprehension

Scaffolding self-regulated learning and metacognition - Implications for the design of computer-based scaffolds

Is adaptive scaffolding effective in facilitating students ability to regulate their learning of complex science topics with hypermedia? We examined the role of different scaffolding instructional interventions in facilitating students shift to more sophisticated mental models as indicated by both performance and process data. Undergraduate students ðN ¼ 51Þ were randomly assigned to one of three scaffolding conditions (adaptive scaffolding [AS], fixed scaffolding [FS], and no scaffolding [NS]) and were trained to use a hypermedia environment to learn about the circulatory system. Pretest, posttest, and verbal protocol data were collected. Findings revealed that the AS condition facilitated the shift in learners mental models significantly more than did the other comparison conditions. Participants in the AS condition regulated their learning by activating prior knowledge, monitoring their emerging understanding by using several strategies, and engaging in adaptive help-seeking. Learners in the FS and NS conditions were less effective at regulating their learning and exhibited great variability in self-regulation of their learning during the knowledge construction activity. We discuss how the findings can be used to inform the design of MetaCogni

Roger Azevedo

Papers

Does training on self-regulated learning facilitate students' learning with hypermedia?

Using Hypermedia as a Metacognitive Tool for Enhancing Student Learning? The Role of Self-Regulated Learning

Testing and refining the direct and inferential mediation model of reading comprehension

Scaffolding self-regulated learning and metacognition - Implications for the design of computer-based scaffolds

Does adaptive scaffolding facilitate students ability to regulate their learning with hypermedia? q