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

人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

As MOOCs grow in popularity, the relatively low completion rates of learners has been a central criticism. This focus on completion rates, however, reflects a monolithic view of disengagement that does not allow MOOC designers to target interventions or develop adaptive course features for particular subpopulations of learners. To address this, we present a simple, scalable, and informative classification method that identifies a small number of longitudinal engagement trajectories in MOOCs. Learners are classified based on their patterns of interaction with video lectures and assessments, the primary features of most MOOCs to date.In an analysis of three computer science MOOCs, the classifier consistently identifies four prototypical trajectories of engagement. The most notable of these is the learners who stay engaged through the course without taking assessments. These trajectories are also a useful framework for the comparison of learner engagement between different course structures or instructional approaches. We compare learners in each trajectory and course across demographics, forum participation, video access, and reports of overall experience. These results inform a discussion of future interventions, research, and design directions for MOOCs. Potential improvements to the classification mechanism are also discussed, including the introduction of more fine-grained analytics.

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Deconstructing disengagement: analyzing learner subpopulations in massive open online courses

One distinctive feature of any adaptive system is the user model that represents essential information about each user This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user modeling

User models for adaptive hypermedia and adaptive educational systems

目的 探讨CD25单抗在无血缘关系造血干细胞移植（UHSCT）中对干细胞植入和移植物抗宿主病（GVHD）的作用。方法 27例UHCST中，移植后1、4d给予CD25单抗1mg/kg。结果 27例中，除1例早期死亡外，26例患者造血全部重建。17例发生急性GVHD，其中Ⅱ度以上急性GVHD6例（23％）。复发3例，严重感染3例。26例可评价患者中，19例无病生存（73％）。结论 CD25单抗在UHSCT中对保证干细胞植入和预防GVHD有肯定的作用，白血病复发并不增加。此研究为UHCST和HLA不合的造血干细胞移植提供一个可选择的途径。

Student assessment is a very important issue in educational settings. The goal of this work is to develop a web-based tool to assist teachers and instructors in the assessment process. Our system is called SIETTE, and its theoretical bases are Computer Adaptive Testing and Item Response Theory. With SIETTE, teachers worldwide can define their tests, and their students can take these tests on-line. The tests are generated according to teachers' specifications and are adaptive, that is, the questions are selected intelligently to fit the student's level of knowledge. In this way, we obtain more accurate estimations of student's knowledge with significantly shorter tests. By using the computer, larger question databases can be stored, selection algorithms can be performed efficiently, and questions can include multimedia content. The use of Java applets allows the inclusion of executable content in question stem and/or answers, so the student can interact with the system by means of this applet. In this way, new possibilities are added to Computer Adaptive Tests, such as using traditional multiple-choice questions together with questions whose answer is evaluated by the applet itself.

SIETTE: A Web-Based Tool for Adaptive Testing

Bayesian networks are graphical modeling tools that have been proven very powerful in a variety of application contexts. The purpose of this paper is to provide education practitioners with the background and examples needed to understand Bayesian networks and use them to design and implement student models. The student model is the key component of any adaptive tutoring system, as it stores all the information about the student (for example, knowledge, interest, learning styles, etc.) so the tutoring system can use this information to provide personalized instruction. Basic and advanced concepts and techniques are introduced and applied in the context of typical student modeling problems. A repertoire of models of varying complexity is discussed. To illustrate the proposed methodology a Bayesian Student Model for the Simplex algorithm is developed.

Bayesian networks for student model engineering

In this paper, we present a new approach to diagnosis in student modeling based on the use of Bayesian Networks and Computer Adaptive Tests. A new integrated Bayesian student model is defined and then combined with an Adaptive Testing algorithm. The structural model defined has the advantage that it measures students' abilities at different levels of granularity, allows substantial simplifications when specifying the parameters (conditional probabilities) needed to construct the Bayesian Network that describes the student model, and supports the Adaptive Diagnosis algorithm. The validity of the approach has been tested intensively by using simulated students. The results obtained show that the Bayesian student model has excellent performance in terms of accuracy, and that the introduction of adaptive question selection methods improves its behavior both in terms of accuracy and efficiency.

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A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation

Collaborative problem-solving (CPS) is a fundamental skill for success in modern societies, and part of many common constructivist teaching approaches. However, its effective implementation and evaluation in both digital and physical learning environments are challenging for educators. This paper presents an original method for identifying differences in students' CPS behaviours when they are taking part in face-to-face practice-based learning (PBL). The dataset is based on high school and university students' hand position and head direction data, which can be automated deploying existing multimodal learning analytics systems. The framework uses Nonverbal Indexes of Students' Physical Interactivity (NISPI) to interpret the key parameters of students' CPS competence. The results show that the NISPI framework can be used to judge students' CPS competence levels accurately based on their non-verbal behaviour data. The findings have significant implications for design, research and development of educational technology.

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Eva Millán

Papers

User models for adaptive hypermedia and adaptive educational systems

SIETTE: A Web-Based Tool for Adaptive Testing

Bayesian networks for student model engineering

A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation

The NISPI framework: Analysing collaborative problem-solving from students' physical interactions