Educational Concept Maps for Personalized Learning Path Generation

Adorni, Giovanni; Koceva, Frosina

doi:10.1007/978-3-319-49130-1_11

Cited by 11 publications

(11 citation statements)

References 8 publications

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“…Educational concept map (ECM; Adorni & Koceva, ) is also used successfully in CG methods. As an example of ECM based method, we can refer to Adorni and Koceva method that is presented in (Adorni & Koceva, ). In their method, a learner first determines his/her knowledge background by selecting a set of topics from an ECM, which results in trimming the known topics from the ECM.…”

Section: Related Workmentioning

confidence: 99%

Estimating time and score uncertainty in generating successful learning paths under time constraints

2018

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This paper addresses the problem of course (path) generation when a learner's available time is not enough to follow the complete course. We propose a method to recommend successful paths regarding a learner's available time and his/her knowledge background. Our recommender is an instance of long term goal recommender systems (LTRS). This method, after locating a target learner in a course graph, applies a depth‐first search algorithm to find all paths for the learner given a time limitation. In addition, our method estimates learning time and score for all paths. It also indicates the probability of error for the estimated time and score for each path. Finally, our method recommends a path that satisfies the learner's time restriction while maximizing expected learning score. In order to evaluate our proposals for time and score estimation, we used the mean absolute error and average MAE. We have evaluated time and score estimation methods, including one proposed in the literature, on two E‐learning datasets.

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Section: Related Workmentioning

confidence: 99%

Estimating time and score uncertainty in generating successful learning paths under time constraints

2018

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“…This section reports some aspects of the formal definition of the ECM model [1] underlying the knowledge base and the PLP generation. An ECM represents a general structure of the subject matter domain that explicits the concepts, their learning materials and the hierarchical and associative relations that connect the concepts and forms the ECM.…”

Section: Ecm Knowledge Basementioning

confidence: 99%

“…This linearization preserves the precedence of the prerequisites defined by the is_req relationship. E.g., if TOPIC_X, representing the concept "Automata", is a propaedeutic requirement of the TOPIC_Y, representing the concept "Turing Machine", in the map exists an association of type is_req between the two concepts (nodes); instead, in the linearized sequence this means that TOPIC_X precedes TOPIC_Y [1].…”

Section: Confirmation Questionsmentioning

confidence: 99%

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Toward a User-Adapted Question/Answering Educational Approach

Alzetta

Adorni

Celik

et al. 2018

Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

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This paper addresses the design of a model for Question/Answering in an interactive and mobile learning environment. The learner's question can be made through vocal interaction or typed text and the answer is the generation of a personalized learning path. This takes into account the focus and type of the question and some personal features of the learner extracted both from the question and prosodic features, in case of vocal questions. The response is a learning path that preserves the precedence of the prerequisite relations and contains all the relevant concepts for answering the user's question. The main contribution of the paper is to investigate the possibility to exploit educational concept maps in a Q/A interactive learning system.

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“…eir personalization process starts with a self-verication of knowledge by a learner which cause pruning the known topics from the ECM map. e output of pruning process will be checked, and nally, by specifying the initial and target point (topic) by the learner, the paths will be generated using ENCODE that performs an algorithm to linearize the map [2].…”

Section: Related Workmentioning

confidence: 99%

Rutico

Nabizadeh

Jorge

Leal

2017

Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

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Nowadays using E-learning platforms such as Intelligent Tutoring Systems (ITS) that support users to learn subjects are quite common. Despite the availability and the advantages of these systems, they ignore the learners' time limitation for learning a subject. In this paper we propose RUTICO, that recommends successful learning paths with respect to a learner's knowledge background and under a time constraint. RUTICO, which is an example of Long Term goal Recommender Systems (LTRS), aer locating a learner in the course graph, it utilizes a Depth-rst search (DFS) algorithm to nd all possible paths for a learner given a time restriction. RUTICO also estimates learning time and score for the paths and nally, it recommends a path with the maximum score that satises the learner time restriction. In order to evaluate the ability of RUTICO in estimating time and score for paths, we used the Mean Absolute Error and Error. Our results show that we are able to generate a learning path that maximizes a learner's score under a time restriction.

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Educational Concept Maps for Personalized Learning Path Generation

Cited by 11 publications

References 8 publications

Estimating time and score uncertainty in generating successful learning paths under time constraints

Estimating time and score uncertainty in generating successful learning paths under time constraints

Toward a User-Adapted Question/Answering Educational Approach

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