Generating reading orders over document collections

Koutrika, Georgia; Liu, Lei; Simske, Steve

doi:10.1109/icde.2015.7113310

Cited by 17 publications

(10 citation statements)

References 21 publications

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“…The learning gaps are identified by constructing a hierarchical prerequisite map. A tool is proposed [9] to decide the sequence in which documents should be read, with documents having basic (general) concept to be read first followed by the documents covering the advanced (specific) concepts. The documents are multiple Wikipedia pages.…”

Section: Literature Reviewmentioning

confidence: 99%

Edu APCCM Automatic Programming Code Constructs Mining from Learning Content

Jhaveri¹,

Pareek²

2020

IJEAT

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The current education ecosystem is moving towards centralized online blended learning. Online learning repositories have replaced traditional libraries.Learning repositories contain learning materials, which can be located with the help of associated metadata. Associating metadata to the content (definition, program, example, figure, and table) of individual learning concept (topic) from the learning material also leads to a better search. If a student knows the prerequisites of the topic s/he wants to learn then the study of current topic would be more fruitful. The prerequisites of a computer science topic can be obtained from its explanation and the programming code snippet used for its implementation. This paper proposes a metadata "code construct as a prerequisite of a code snippet". For example "recursion and function call are prerequisite to understand recursive module of binary tree traversal". It also proposes the framework to automatically identify, extract and present the code constructs used in code snippets included in a computer science learning material. Thus obtained list of code constructs act as prerequisites for understanding the corresponding code snippet. Rule-based pattern mining approach is used for the identification of code snippet in the learning material and identification of code constructs in the code snippet.A pattern set is designed for the same. Natural language tool kit of python is used to identify the code snippet. The algorithms are tested on the programs of C, C++ and Java. Accuracy and efficiency of the developed algorithms is checked against the manual results given by subject experts. An average F1 score of 92% is obtained.

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Section: Literature Reviewmentioning

confidence: 99%

Edu APCCM Automatic Programming Code Constructs Mining from Learning Content

Jhaveri¹,

Pareek²

2020

IJEAT

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“…Range of topic coverage: We first measure the learning level of a concept based on the range of topics covered by the concept [3]. Essentially, the more topics that a concept covers, the more basic the concept is.…”

Section: Prerequisite Relation Identificationmentioning

confidence: 99%

Prerequisite Concept Maps Extraction for AutomaticAssessment

Wang

Liu

2016

Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion

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Traditional assessment modes usually give identical set of questions to each student, thus are inefficient for students to fix their problems. In order to perform an efficient assessment, we utilize prerequisite concept maps to find students' learning gaps and work on closing these gaps and proposed a two-phase model for concept map construction. Experiments on concept pairs with prerequisite relationships which are manually created show the promise of our proposed method. In order to meet the challenge of using concept maps in automatic assessments, we also derive a top-k concept selection algorithm which allows students to view different numbers of concepts. Copyright is held by the International World Wide Web Conference Committee (IW3C2). IW3C2 reserves the right to provide a hyperlink to the author's site if the Material is used in electronic media.

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“…e prerequisite relation exists as a natural precedence among concepts in cognitive processes when people learn, organize, apply, and generate knowledge [1]. Concept prerequisite learning has played an important role in many fields, including precedence relation identification [2][3][4], concept graph extraction [5,6], reading list generation [7,8], curriculum planning [9], course sequencing [10], course design [11], knowledge tracing [12], and automatic assessment [13].…”

Section: Introductionmentioning

confidence: 99%

Mining Precedence Relations among Lecture Videos in MOOCs via Concept Prerequisite Learning

Xiao

Bai

Wang

2021

Mathematical Problems in Engineering

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In recent years, MOOC has gradually become an important way for people to learn knowledge. But the knowledge background of different people is quite different. Moreover, the precedence relations between lecture videos in a MOOC are often not clearly explained. As a result, some people may encounter obstacles due to lack of background knowledge when learning a MOOC. In this paper, we proposed an approach for mining precedence relations between lecture videos in a MOOC automatically. First, we extracted main concepts from video captions automatically. And then, an LSTM-based neural network model was used to measure prerequisite relations among the main concepts. Finally, the precedence relations between lecture videos were identified based on concept prerequisite relations. Experiments showed that our concept prerequisite learning method outperforms the existing methods and helps accurately identify the precedence relations between lecture videos in a MOOC.

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Generating reading orders over document collections

Cited by 17 publications

References 21 publications

Edu APCCM Automatic Programming Code Constructs Mining from Learning Content

Edu APCCM Automatic Programming Code Constructs Mining from Learning Content

Prerequisite Concept Maps Extraction for AutomaticAssessment

Mining Precedence Relations among Lecture Videos in MOOCs via Concept Prerequisite Learning

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