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NavEx: Providing Navigation Support for Adaptive Browsing of Annotated Code Examples.
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This paper presents NavEx, an adaptive environment for accessing interactive programming examples. NavEx implements a specific kind of adaptive navigation support known as adaptive annotation. The classroom study of NavEx confirmed that adaptive navigation support can visibly increase student motivation to work with non-mandatory educational content. NavEx boosted the overall amount of work and the average length of a session. In addition, various features of NavEx were highly regarded by the students.
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... Decision trees, k-nearest neighbors and Bayesian Networks (BN) are the most commonly used predictive mining methods in ITS systems [10,11]. Research on existing EBLbased ITS [12,13,14] has gone a long way in simulating the role of a teacher in many ways, but there are still concerns about design of a formal framework that can extract features from domain examples and tasks in terms of basic learning units, represent them in an efficient and scalable manner and present a personalized list of examples to students. ...
... It provided help to students by presenting sideby-side examples and highlighting their common structural components. This paper elaborates the methodologies used in the framework used by NavEx [12] and PADS [13] that are very similar in their objective of adaptively finding and presenting most relevant examples to students, yet very different in the methodologies used. NavEx and PADS and other existing systems cited in this paper are only a part of an ITS. ...
... KE can be done either automatically or manually. NavEx [12] uses parsers and syntax tree representation to automatically extract concepts from C programs. It transforms each worked-out example (WE), given as a C program, into its equivalent syntax tree, and then parses this tree to extract the WE's main concepts. ...
... This way, students transfer learned ways of solving problems to new problems of the same type. Yudelson and Brusilovsky [2] applied this tutoring approach for building learning systems for programming. NavEx [2] is a web-based tool for exploring programming examples. ...
... Yudelson and Brusilovsky [2] applied this tutoring approach for building learning systems for programming. NavEx [2] is a web-based tool for exploring programming examples. Each example is annotated with textual explanations for each important line. ...
In this paper, we review tutoring approaches of computer-supported systems for learning programming. From the survey we have learned three lessons. First, various AI-supported tutoring approaches have been developed and most existing systems use a feedback-based tutoring approach for supporting students. Second, the AI techniques deployed to support feedback-based tutoring approaches are able to identify the student's intention, i.e. the solution strategy implemented in the student solution. Third, most reviewed tutoring approaches only support individual learning. In order to fill this research gap, we propose an approach to pair learning which supports two students who solve a programming problem face-to-face.
... A smart learning environment may provide adaptive support in many forms, including curriculum sequencing or navigation [1], student-centered e-learning settings [2], or intelligent support for problem solving [3]. For the latter class, a smart learning environment should be able to provide students with appropriate problems and intervene in the process of problem solving when necessary. ...
Researchers of constructivist learning suggest that students should rather learn to solve real-world problems than artificial problems. This paper proposes a smart constructivist learning environment which provides real-world problems collected from crowd-sourcing problem-solution exchange platforms. In addition, this learning environment helps students solve real-world problems by retrieving relevant information on the Internet and by generating appropriate questions automatically. This learning environment is smart from three points of view. First, the problems to be solved by students are real-world problems. Second, the learning environment extracts relevant information available on the Internet to support problem solving. Third, the environment generates questions which help students to think about the problem to be solved.
... There are other programming environments that also suggest examples to users [9], [10]. However, I am not aware of any systems that suggest programming example based on a learner's existing knowledge. ...
Programmers often re-appropriate code or new programming skills they find in unfamiliar code within their own programs [1], [2]. This process enables programmers, including novices, to acquire new programming knowledge while working on their own programming projects. Unfortunately, novice programmers often have difficultly understanding and integrating existing code into their own programs [3], thereby limiting acquisition of new programming concepts found within unfamiliar code. In this paper, I describe my prior work attempting to decrease the burden of learning new programming concepts found in unfamiliar code with automatically generated programming tutorials. Later, I introduce my proposal to create a programming environment that adapts to the skill level of the programmer while introducing programming concepts found within existing code by suggesting example code alongside programming puzzles.
... A few projects that applied parameterized question generation in e-learning systems for programming domain [12][13][14]. A range of architectures for exploring adaptive technology on dedicated adaptive interfaces has been reported in [15][16]. The added value of adaptive link annotation for an elearning portal has also been studied in [17]. ...
This paper reports a novel visual metaphor of parallel views on open student models and a new design of the IntrospectiveViews interface implementing this metaphor. The interface allows visualizing not only the student's own model, but also displaying parallel views on the models of her peers and the cumulate model of the entire class or group. In the paper, we describe the application of parallel IntrospectiveViews for visualizing models representing student progress on the QuizJET parameterized questions.
Online programming discussion forums are popular trouble-shooting and problem-solving sites for programmers and learners to reach out for help. The massive volumes of forum threads harbor tremendous amounts of information, but at the same time increase the complexity of search and navigation. In this work, we make use of programming discussions' syntactic, semantic and social features to model content associated with learning activities based on the ICAP learning framework. Our main goal is to detect useful content for learning programming in a large scale of questions and answers, while at the same time experiment with an artificial intelligence approach to detect learning-inductive content. We build regression models based on the defined constructive learning activities. Results reveal a passive-proactive learning behavior in an online programming discussion forum. The findings also reconfirm the value of programming discussion content, disregarding the crowds' approval. The automatic detection of constructive learning activities from programming discussions can be a helpful classifier in identifying relevant educational resources. Overall, this project contributes to our understanding on analyzing and utilizing mass programming discussion content for online programming language learning.
An Intelligent Tutoring System (ITS) provides direct customized instruction or feedback to students while they perform a task in a tutoring system without the intervention of a human. One of the main functions of an ITS system is to present its students with course materials that are most appropriate to their current knowledge of domain concepts, example being one of the course materials. ITS systems typically compare and analyze student model (SM) components for student's current knowledge of concepts (main topics, e.g. Scanf in C programming) that are required to understand the next example (e.g. Codes for scanf) suitable for learning a task (e.g. Write C code to read 2 integers from the keyboard). Existing systems such as NavEx and PADS perform an exhaustive matching of student knowledge level with all examples in the database. This research proposes a task-based technique for managing and classifying examples for more effective retrieval of relevant examples for learning a task. We propose a system called EASK for translating task and example solutions into concepts for similarity matching, which is more readily available, easily extendible and adaptable to other domains. Examples and tasks are represented as vectors of weights computed with term frequency measure TFIDF that signify the importance of a concept for an example. Examples most similar to a task are found by using a classification method called k-NN, which finds the closeness between different objects such as examples and tasks using cosine similarity measure and selecting the k objects (examples) with highest similarity scores. As a by-product, k-NN also predicts the class label (difficulty level) of the task. Our proposed model achieves this prediction with 89% accuracy.
Adaptive hypermedia is one of the most popular approaches of personalized information access. When the field started to emerge, the expectation was that soon nearly all published hypermedia content could be adapted to the needs, preferences, and abilities of its users. However, after a decade and a half, the gap between the amount of total hypermedia content available and the amount of content available in a personalized way is still quite large.In this work we are proposing a novel way of speeding the development of new adaptive hypermedia systems. The gist of the approach is to extract the adaptation functionality out of the adaptive hypermedia system, encapsulate it into a standalone system, and offer adaptation as a service to the client applications. Such a standalone adaptation provider reduces the development of adaptation functionality to configuration and compliance and as a result creates new adaptive systems faster and helps serve larger user populations with adaptively accessible content.To empirically prove the viability of our approach, we developed PERSEUS - server of adaptation functionalities. First, we confirmed that the conceptual design of PERSEUS supports realization of a several of the widely used adaptive hypermedia techniques. Second, to demonstrate that the extracted adaptation does not create a significant computational bottleneck, we conducted a series of performance tests. The results show that PERSEUS is capable of providing a basis for implementing computationally challenging adaptation procedures and compares well with alternative, not-encapsulated adaptation solutions. As a result, even on modest hardware, large user populations can be served content adapted by PERSEUS.
This paper discusses the need to provide adaptive navigational support for students accessing large numbers of interactive examples in Web-enhanced education. Here we introduce the system NavEx (Navigation to Examples), which is able to provide the adaptive annotation of programming examples without the need for manual indexing. NavEx uses innovative algorithms for the prerequisite-outcome indexing of learning materials and is an efficient knowledge-based approach for adaptive annotation. The system serves as an adaptive value-added service residing between an learning portal and an advanced re-usable content. 1. Web-based Examples in a Programming Course It has often been claimed that humans use solutions to previous problems to solve new problems or planning tasks. This is especially pertinent to the domain of programming. Both experienced and novice programmers often use program examples they have created or learned in the past to solve new programming tasks. Experienced teachers of programming courses present and explain one or more carefully chosen example programs during every lecture. In Web-based and Web-enhanced education the code of these examples is provided on the Web, along with other kinds of learning material, in order for the students to study and run the example code on their own. Our system, WebEx (Brusilovsky 2001), attempts to integrate benefits of these two approaches (instructor explanation and web interactivity) into example presentation. WebEx is able to serve interactive, explained examples via the Web. The idea of WebEx is simple: the author or later the instructor using the example code can provide textual explanations for every line of the program code. The students can browse these comments at their own pace and sequence, by selectively clicking on the commented lines (Figure 1).
A well-known challenge of adaptive educational systems is the need to develop intelligent content, which is very time and expertise consuming. In traditional approaches a teacher is kept at a distance from intelligent authoring. This paper advocates the involvement of teachers in creating intelligent content . We are presenting an approach to the development of intelligent content as well as an authoring tool for teachers that support our approach. This approach has two main stages: elicitation of concepts from content elements and the identification of a prerequisite/outcome structure for the course. The resulting sequence of adaptive activities reflects the author's view of the course's organization. The developed tool facilitates concept elicitation in two ways: it provides an author with an automatic indexing component and also allows her/him to edit the index using the domain ontology as an authoring map.
In this paper we present an Adaptive Educational Hypermedia prototype, named INSPIRE. The approach employed in INSPIRE emphasizes the fact that learners perceive and process information in very different ways, and integrates ideas from theories of instructional design and learning styles. Our aim is to make a shift towards a more "learning-focused" paradigm of instruction by providing a sequence of authentic and meaningful tasks that matches learners' preferred way of studying. INSPIRE, throughout its interaction with the learner, dynamically generates learner-tailored lessons that gradually lead to the accomplishment of learner's learning goals. It supports several levels of adaptation: from full system-control to full learner-control, and offers learners the option to decide on the level of adaptation of the system by intervening in different stages of the lesson generation process and formulating the lesson contents and presentation. Both the adaptive and adaptable behavior of INSPIRE are guided by the learner model which provides information about the learner, such as knowledge level on the domain concepts and learning style. The learner model is exploited in multiple ways: curriculum sequencing, adaptive navigation support, adaptive presentation, and supports system's adaptable behavior. An empirical study has been performed to evaluate the adaptation framework and assess learners' attitudes towards the proposed instructional design.
This paper discusses the problems of developing versatile adaptive and intelligent learning systems that can be used in the context of practical Web-based education. We argue that versatility is an important feature of successful Web-based education systems. We introduce ELM-ART, an intelligent interactive educational system to support learning programming in LISP. ELM-ART provides all learning material online in the form of an adaptive interactive textbook. Using a combination of an overlay model and an episodic student model, ELM-ART provides adaptive navigation support, course sequencing, individualized diagnosis of student solutions, and example-based problem-solving support. Results of an empirical study show different effects of these techniques on different types of users during the first lessons of the programming course. ELM-ART demonstrates how some interactive and adaptive educational components can be implemented in WWW context and how multiple components can be naturally integrated together in a single system. (http://aied.inf.ed.ac.uk/members01/archive/vol_12/weber/full.html)
Adaptive link annotation is a new direction within the field of user-model based interfaces. It is a specific technique in Adaptive Navigation Support (ANS) whose aim is to help users find an appropriate path in a learning and information space by adapting link presentation to the goals, knowledge, and other characteristics of an individual user. More specifically, ANS has been implemented on the WWW in the InterBook system as link annotation indicating several states such as visited, ready to be learned, or not ready to be learned. These states represent an expert's suggested path for an individual user through a learning space according to both a history-based (tracking where the user has been), and a prerequisite based (indexing of content as a set of domain model concepts) annotation. This particular process has been more fully described elsewhere [Brusilovsky, Eklund & Schwarz 1998]. This paper details results from an investigation to determine the effectiveness of usermodel bas...
This paper is devoted to evaluation of adaptive navigation support in educational context. We present an educational hypermedia system ISIS-Tutor that applies several ANS technologies -- adaptive annotation, adaptive hiding, and direct guidance -- and describe a study, which evaluates the first two technologies. The results show that adaptive navigation support is helpful and can reduce user navigation efforts
ELM-ART: An adaptive versatile system for Web-based instruction Special Issue on Adaptive and Intelligent Web-based Educational Systems, 351-384, also available at http://cbl.leeds.ac.uk/ijaied/abstracts/Vol_12/weber.html An adaptive E-learning service for accessing Interactive examples
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. Weber, G., Brusilovsky, P. (2001) ELM-ART: An adaptive versatile system for Web-based instruction. In P. Brusilovsky and C. Peylo (eds.), International Journal of Artificial Intelligence in Education 12 (4), Special Issue on Adaptive and Intelligent Web-based Educational Systems, 351-384, also available at http://cbl.leeds.ac.uk/ijaied/abstracts/Vol_12/weber.html. [4]. Brusilovsky, P., Yudelson, M., and Sosnovsky, S. (2004) An adaptive E-learning service for accessing Interactive examples. In: J. Nall and R. Robson (eds.) Proceedings of World Conference on E-Learning, E-Learn 2004, Washington, DC, USA, November 1-5, 2004, AACE, pp. 2556-2561.
Adaptation in open corpus hypermedia Special Issue on Special Issue on Adaptive and Intelligent Web-based Educational Systems, 325-350, also available at http://cbl.leeds.ac.uk/ijaied/abstracts/Vol_12/henze
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. Henze, N., Nejdl, W. (2001) Adaptation in open corpus hypermedia. In P. Brusilovsky and C. Peylo (eds.), International Journal of Artificial Intelligence in Education 12 (4), Special Issue on Special Issue on Adaptive and Intelligent Web-based Educational Systems, 325-350, also available at http://cbl.leeds.ac.uk/ijaied/abstracts/Vol_12/henze.html.
KnowledgeTree: A distributed architecture for adaptive e-learningAlternate track papers and posters)
- P Brusilovsky
. Brusilovsky, P. (2004) KnowledgeTree: A distributed architecture for adaptive e-learning. In: Proceedings of The Thirteenth International World Wide Web Conference, WWW 2004 (Alternate track papers and posters), New York, NY, 17-22 May, 2004, ACM Press, pp. 104-113.