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

The power of feedback.

This paper reviews peer-reviewed empirical studies on gamification. We create a framework for examining the effects of gamification by drawing from the definitions of gamification and the discussion on motivational affordances. The literature review covers results, independent variables (examined motivational affordances), dependent variables (examined psychological/behavioral outcomes from gamification), the contexts of gamification, and types of studies performed on the gamified systems. The paper examines the state of current research on the topic and points out gaps in existing literature. The review indicates that gamification provides positive effects, however, the effects are greatly dependent on the context in which the gamification is being implemented, as well as on the users using it. The findings of the review provide insight for further studies as well as for the design of gamified systems.

/pdf/does-gamification-work-a-literature-review-of-empirical-3d9hukqa8z.pdf

Does Gamification Work? -- A Literature Review of Empirical Studies on Gamification

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

In this demo paper, we present a visual approach for explaining learning content recommendation in the personalized practice system Mastery Grids. The proposed approach uses a concept-level visualization of student knowledge in Java programming to demonstrate why specific practice content is recommended by the system. The visualized student knowledge is estimated by a Bayesian Knowledge Tracing approach, which traces student problem-solving performance. The visual explanatory components, which show both a fine-grained and aggregated knowledge level, are presented to the students along with textual explanations. The goal of this approach is to display the suitability of each recommended item in the context of a student's current knowledge and goal, i.e., the current topic they are studying.

https://par.nsf.gov/servlets/purl/10107288

Explaining educational recommendations through a concept-level knowledge visualization

Invited Papers.- Enhancing Adaptive Hypermedia Presentation Systems 1 by Lifelike Synthetic Characters.- Full Papers.- Dynamic Generation of Adaptive Web Catalogs.- An Intelligent Tutor for a Web-Based Chess Course.- Adapting Web-Based Information to the Needs of Patients with Cancer.- Group User Models for Personalized Hyperlink Recommendations.- Adaptive Navigation Support and Adaptive Collaboration Support in WebDL.- Case-Based User Profiling for Content Personalisation.- Providing Tailored (Context-Aware) Information to City Visitors.- Adding Adaptive Features to Virtual Reality Interfaces for E-Commerce.- WAPing the Web: Content Personalisation for WAP-Enabled Devices.- Extendible Adaptive Hypermedia Courseware: Integrating Different Courses and Web Material.- Logically Optimal Curriculum Sequences for Adaptive Hypermedia Systems.- Towards Zero-Input Personalization: Referrer-Based Page Prediction.- LiveInfo: Adapting Web Experience by Customization and Annotation.- Adaptivity for Conceptual and Narrative Flow in Hyperbooks: The MetaLinks System.- The MacroNode Approach: Mediating Between Adaptive and Dynamic Hypermedia.- ECHOES: An Immersive Training Experience.- A Connectionist Approach for Supporting Personalized Learning in a Web-Based Learning Environment.- Adaptive Hypertext Design Environments: Putting Principles into Practice.- ECSAIWeb: A Web-Based Authoring System to Create Adaptive Learning Systems.- Adaptive Content in an Online Lecture System.- A Web-Based Socratic Tutor for Trees Recognition.- Adaptation Control in Adaptive Hypermedia Systems.- Short Papers.- An Agent-Based Approach to Adaptive Hypermedia Using a Link Service.- Adaptive Testing by Test++.- What Does the User Want to Know About Web Resources? A User Model for Metadata.- Web Information Retrieval for Designing Distance Hypermedia Courses.- Formative Evaluation of Adaptive CALLware: A Case Study.- How Adaptivity Affects the Development of TANGOW Web-Based Courses.- An Adaptive Web Content Delivery System.- Knowledge Computing Method for Enhancing the Effectiveness of a WWW Distance Education System.- Interface Adaptation to Style of User-Computer Interaction.- Adaptation and Generation in a Web-Based Lisp Tutor.- Collaborative Maintenance in ULYSSES.- An Adaptive Open Hypermedia System on the Web.- Towards an Adaptive Learners' Dictionary.- Concept Filtering and Spatial Filtering in an Adaptive Information System.- Analysing Web Search Logs to Determine Session Boundaries for User-Oriented Learning.- Learning User Profiles in NAUTILUS.- Lexical Chaining for Web-Based Retrieval of Breaking News.- Designing for Social Navigation of Food Recipes.- A Study Comparing the Use of Shaded Text and Adaptive Navigational Support in Adaptive Hypermedia.- Layered Evaluation of Adaptive Applications and Services.- Exploratory Activity Support Based on a Semantic Feature Map.- Adaptivity in AHMED.- An Adaptive Document Generation Based on Matrix of Contents.- Logical Dimensions for the Information Provided by a Virtual Guide.- Automated Collaborative Filtering Applications for Online Recruitment Services.- ConTexts: Adaptable Hypermedia.- Coherence in Modularly Composed Adaptive Learning Documents.- ACE-Adaptive Courseware Environment.- The Adaptive University Calendar.- Sense-Based User Modelling for Web Sites.- Generating Personal Travel Guides from Discourse Plans.- Doctoral Consortium Papers.- Distributed Systems for Group Adaptivity on the Web.- Open Multimedia Environment to Retrieve and Organise Documents: An Adaptive Web-Based IR System in the Field of Textile and Clothing Industry.- Researching Adaptive Instruction.- A Modular Approach for User Modelling.

Adaptive hypermedia and adaptive web-based systems : international conference

This paper presents the results of empirical study of mental representations of the field of HCI, obtained by statistical analysis. Eight HCI experts participating in the study were asked to classify the papers presented at the EWHCI'92 Conference. The results show satisfactory agreement between the experts' classifications, as well as high interpretability of the group data. Some conclusions about the implicit “cognitive map” of the HCI field are discussed in the paper.

https://www.interux.com/publications/Brusilovsky-EWHCI93.pdf

Structuring the Field of HCI: An Empirical Study of Experts' Representations

The increasing utilization of massive open online courses has significantly expanded global access to formal education. Despite the technology's promising future, student interaction on MOOCs is still a relatively under-explored and poorly understood topic. This work proposes a multi-level pattern discovery through hierarchical discriminative tensor factorization. We formulate the problem as a hierarchical discriminant subspace learning problem, where the goal is to discover the shared and discriminative patterns with a hierarchical structure. The discovered patterns enable a more effective exploration of the contrasting behaviors of two performance groups. We conduct extensive experiments on several real-world MOOC datasets to demonstrate the effectiveness of our proposed approach. Our study advances the current predictive modeling in MOOCs by providing more interpretable behavioral patterns and linking their relationships with the performance outcome.

/pdf/iterative-discriminant-tensor-factorization-for-behavior-56pb9n5x3d.pdf

Iterative Discriminant Tensor Factorization for Behavior Comparison in Massive Open Online Courses

Information visualizations can be applied to the educational domain in order to help the students access appropriate educational resources. We present a novel adaptive visualization method that supports navigations through class materials according to the lecture topics. The map-based adaptive annotations and the relevance-based visualizations are supported in the framework. We are going to use the system in a real iSchool class and will conduct a user study in order to validate the effectiveness of the idea.

Peter Brusilovsky

Papers

Explaining educational recommendations through a concept-level knowledge visualization

Adaptive hypermedia and adaptive web-based systems : international conference

Structuring the Field of HCI: An Empirical Study of Experts' Representations

Iterative Discriminant Tensor Factorization for Behavior Comparison in Massive Open Online Courses

Guiding educational resources for iSchool students with topic-based adaptive visualization