The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends.

Text mining in education

Authoring tools have been broadly used to design Intelligent Tutoring Systems (ITS). However, ITS community still lacks a current understanding of how authoring tools are used by non-programmer authors to design ITS. Hence, the objective of this work is to review how authoring tools have been supporting ITS design for non-programmer authors. In order to meet our goal, we conduct a Systematic Literature Review (SLR) to identify the primary studies on the use of ITS authoring tools, following a pre-defined review protocol. Among the 4622 papers retrieved from seven digital libraries published from 2009 to June 2016, 33 papers are finally included after applying our exclusion and inclusion criteria. We then identify the main ITS components authored, the ITS types designed, the features used to facilitate the authoring process, the technologies used to develop authoring tools and the time at which authoring occurs. We also look for evidence of the benefits of ITS authoring tools. In summary, the main findings of this work are: (1) there is empirical evidence of the benefits (i.e., mainly in terms of effectiveness, efficiency, quality of authored artifacts, and usability) of using ITS authoring tools for non-programmer authors, specially to aid authoring of learning content and to support authoring of model-tracing/cognitive and example-tracing tutors; 2) domain and pedagogical models have been much more targeted by authoring tools; (3) several ITS types have been authored, with an emphasis on model-tracing/cognitive and example-tracing tutors; (4) besides providing features for authoring all four ITS components, current authoring tools are also presenting general features (e.g., view learners’ statistics and reuse tutor design) to create broader authoring tools; (5) a great diversity of technologies, which include AI techniques, software solutions and distributed technologies, are used to develop ITS authoring tools; and (6) authoring tools have been mainly targeting ITS design before students’ instruction, but works are also addressing authoring during and/or post-instruction relying both on human and artificial intelligence. We conclude this work by showing several promising research opportunities that are quite important and interesting but underexplored in current research and practice.

/pdf/authoring-tools-for-designing-intelligent-tutoring-systems-a-t04proivt9.pdf

Authoring Tools for Designing Intelligent Tutoring Systems: a Systematic Review of the Literature

The advent of high-speed Internet connections has revolutionized the way research is being carried out to obtain relevant information. Conversely, retrieving pertinent information from the copious resources available is not only difficult but also time consuming. In the recent years, tagging activity has been perceived as a potential source of knowledge on personal preferences, interests, targets, goals, and other attributes. Tags allow users to effectively annotate resources using keywords to personalize their recommendations and organize the resources for easy retrieval. However, the preference of users varies extremely resulting in tagging being counterproductive. These shortcomings reduce the application of the tagging system for filtering as well as retrieval of information. The tag recommendation system becomes useful by suggesting a set of relevant keywords to annotate the resources. This paper presents a review of the tag recommendation systems and the constraints that affects the available tag recommendation systems. Furthermore, we propose the use of spreading activation algorithm to study the role of constructed topic ontology for efficient tag recommendations. This approach is founded on the assumption that tags that are recommended to the user are predicted from the extracted keywords from the existing blogs and the topics in constructed topic ontology. We have also proposed a tag classification system, namely Correlation-based Feature Selection-Hybrid Genetic Algorithm and classifier HGA-SVM support vector machine, and have compared the results with results produced by other existing feature selection methods. The results obtained from the experiments have been presented. WIREs Data Mining Knowl Discov 2015, 5:87-112. doi: 10.1002/widm.1149

Data mining-based tag recommendation system: an overview

Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence

A new ontology learning model called domain ontology graph (DOG) is proposed in this paper. There are two key components in the DOG, i.e., the definition of the ontology graph and the ontology learning process. The former defines the ontology and knowledge conceptualization model from the domain-specific text documents; the latter offers the necessary method of semiautomatic domain ontology learning and generates the corresponding ontology graphs. Two kinds of ontological operations are also defined based on the proposed DOG, i.e., document ontology graph generation and ontology-graph-based text classification. The simulation studies focused upon Chinese text data are used to demonstrate the potential effectiveness of our proposed strategy. This is accomplished by generating DOGs to represent the domain knowledge and conducting the text classifications based on the generated ontology graph. The experimental results show that the new method can produce significantly better classification accuracy (e.g., with 92.3% in f-measure) compared with other methods (such as 86.8% in f-measure for the term-frequency-inverse-document-frequency approach). The high performance demonstrates that our presented ontological operations based on the ontology graph knowledge model are effectively developed.

A New Method for Knowledge and Information Management Domain Ontology Graph Model

Annotating learning material with metadata allows easy reusability by different learning/tutoring systems. Several metadata standards have been developed to represent learning objects and courses. A learning system needs to use pedagogic attributes including document type, topic, coverage of concepts, and for each concept the significance and the role. Moreover, in order to have a flexible and reusable repository of e-learning materials, it is necessary that the annotation of the documents with such metadata be done in an automatic fashion as far as possible. This paper describes the attributes that represent some important pedagogic characteristics of learning materials. To reduce the overhead of manual annotation we have explored the feasibility of automatic annotation of learning materials with metadata. This facilitates the creation of an elearning open repository for storing these annotated learning materials, which can be used by learning systems. The automatic annotation is based on a domain knowledge base and a number of algorithms like standard classification algorithms, parsing and analysis of documents have been used for this purpose. The results show a fair degree of accuracy, which may be improved in future using more sophisticated algorithms.

Automatic Extraction of Pedagogic Metadata from Learning Content

Intelligent Tutoring Systems (ITS) are computer-based education tools used for adaptive learning. An ITS authoring system allows teachers to create different modules of an intelligent tutoring system. In this paper, we present an ITS authoring system that allows teachers with very less computer skill to author most important modules of an ITS with minimum efforts. The salient features of the system include authoring multilingual documents, defining teaching strategies using fuzzy rules, representing student models through fuzzy state transition automata. The authoring system was used by the teachers and evaluated. The results shows fair degree of accuracy, which may further be improved by adding few more features.

An authoring system for developing Intelligent Tutoring System

One of the most important components of an e-learning system is the learning material. The popularity of e-learning has led to the development of many learning object repositories that store high quality learning materials specifically created for e-learning. High quality learning materials are expensive to create. So it is very important to ensure reuse of learning materials. Reuse of learning materials are made possible by semantically tagging them with standard metadata. In this chapter, we present a comparative study of available learning object metadata and learning object repositories. The learning material can be tagged either manually or automatically. Manual annotation is a time consuming and expensive process. We have explored the feasibility of tagging learning materials automatically with a set of IEEE LOM metadata specification. Here, we present a standard classification approach using probabilistic neural network to automatically identify the topic of the learning material. The classifier is tested and the result shows a fair degree of accuracy.

A Comparative Study of Learning Object Metadata, Learning

India is a country of diversity in language, culture and socio-economic conditions. In order to make use the cutting edge computer aided education technology in this setting, efforts have to be made for proper management of the learning materials by addressing this diversity. In this paper, we present a framework for manual ontology engineering in education domain for managing learning materials of the curriculum related requirements of school students. We identify an effective way of structuring the knowledge about education domain in a layered architecture. The layered architecture allows us to clearly demarcate the roles of topics, concepts and actual words in a multilingual setting. With the help of the constructed ontology, we provide scheme for indexing educational materials into different layers of ontology for the management of learning materials. Domain specific and personalized search interfaces based on the created index structure have also been provided.

A Framework for Manual Ontology Engineering for Management of Learning Material Repository.

The World Wide Web is composed of a large amount of learning content and an e-learner can retrieve learning materials from the Web. The different learners may have different learning requirements. Because the preferences and learner's requirement can vary greatly across individuals, a personalized retrieval system must be tailored so that it should be able to provide a learner with learning materials that he requires. The retrieval system should decide whether a document is relevant to the learner based on the curriculum requirement, the learner profile and the type of the learning material. We have implemented an information retrieval system for retrieving learning materials to satisfy the learners' need. To retrieve the personalized search results, the system looks into the learner profile, the domain knowledge and the automatically metadata annotated documents retrieves from the Web. To evaluate the performance of the system, many queries were processed out by our system.

A personalized information retrieval module for retrieving learning materials

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