Question & Answering Interface to Improve the Students’ Experience in an E-learning Course with a Virtual Tutor

2020 ◽  
pp. 45-54
Author(s):  
João Balsa ◽  
Luís Neves ◽  
Maria Beatriz Carmo ◽  
Ana Paula Cláudio
Keyword(s):  
Question Answering ◽  
E Learning

A Multi-Agent Question-Answering System for E-Learning and Collaborative Learning Environment

2014 ◽  
pp. 134-149
Author(s):  
Tannaz Alinaghi ◽  
Ardeshir Bahreininejad
Keyword(s):  
Collaborative Learning ◽  
Learning Environment ◽  
Question Answering ◽  
Question Answering System ◽  
Course Materials ◽  
Life Styles ◽  
History Of ◽  
E Learning ◽  
Multi Agent ◽  
Collaborative Learning Environment

The increasing advances of new Internet technologies in all application domains have changed life styles and interactions. E-learning and collaborative learning environment systems are originated through such changes and aim at providing facilities for people in different times and geographical locations to cooperate, collaborate, learn and work together by using various educational services. One of the most important requirements of learners in online and virtual environments is the ability to ask questions and receive appropriate answers. The nature of such environments and the lack of physical existence of teachers make such issues critical and challenging problems. This paper presents a multi-agent system for building a question-answering system in learning management systems and collaborative learning environments. In the proposed system, after validating the content of questions, all available resources including course materials, frequently asked questions and responses from other learners will be gathered and finally using a recommender system, the most appropriate answer(s) with respect to several criteria such as learner’s knowledge, research background, history of previous questions, and the candidate answers relevant to the question will be suggested. A simplified version of the system has been implemented and integrated to a well known open source collaborative learning environment system in order to simulate and evaluate the applicability and appropriateness of the proposed system. The result shows that the proposed question-answering system may be used efficiently and expanded to accommodate further advanced capabilities.

Understanding Romanian Texts by Using Gamification Methods

2019 ◽  
Vol 9 (1) ◽  
pp. 52-57
Author(s):  
Ștefania-Eliza Berghia ◽  
Bogdan Pahomi ◽  
Daniel Volovici
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Choice Theory ◽  
Social Choice Theory ◽  
Parts Of Speech ◽  
Automatic Translation ◽  
E Learning ◽  
Syntactic Function

AbstractIn recent years, there has been increasing interest in the field of natural language processing. Determining which syntactic function is right for a specific word is an important task in this field, being useful for a variety of applications like understanding texts, automatic translation and question-answering applications and even in e-learning systems. In the Romanian language, this is an even harder task because of the complexity of the grammar. The present paper falls within the field of “Natural Language Processing”, but it also blends with other concepts such as “Gamification”, “Social Choice Theory” and “Wisdom of the Crowd”. There are two main purposes for developing the application in this paper:a) For students to have at their disposal some support through which they can deepen their knowledge about the syntactic functions of the parts of speech, a knowledge that they have accumulated during the teaching hours at schoolb) For collecting data about how the students make their choices, how do they know which grammar role is correct for a specific word, these data being primordial for replicating the learning process

Using a User-Interactive QA System for Personalized E-Learning

2010 ◽  
pp. 239-257
Author(s):  
Hu Dawei ◽  
Chen Wei ◽  
Zeng Qingtian ◽  
Hao Tianyong ◽  
Min Feng
Keyword(s):  
User Modeling ◽  
Question Answering ◽  
Personal Information ◽  
Course Content ◽  
Concept Hierarchy ◽  
Modeling Approach ◽  
Answer Extraction ◽  
E Learning ◽  
Interactive Question ◽  
Historical Question

A personalized e-learning framework based on a user-interactive question-answering (QA) system is proposed, in which a user-modeling approach is used to capture personal information of students and a personalized answer extraction algorithm is proposed for personalized automatic answering. In our approach, a topic ontology (or concept hierarchy) of course content defined by an instructor is used for the system to generate the corresponding structure of boards for holding relevant questions. Students can interactively post questions, and also browse, select, and answer others’ questions in their interested boards. A knowledge base is accumulated using historical question/answer (Q/A) pairs for knowledge reuse. The students’ log data are used to build an association space to compute the interest and authority of the students for each board and each topic. The personal information of students can help instructors design suitable teaching materials to enhance instruction efficiency, be used to implement the personalized automatic answering and distribute unsolved questions to relevant students to enhance the learning efficiency. The experiment results show the efficacy of our user-modeling approach.

scholarly journals “Ask, Acquire, Appraise”: A Study of LIS Practitioners Participating in an EBLIP Continuing Education Course

2013 ◽  
Vol 8 (2) ◽  
pp. 200 ◽  
Author(s):  
Anthea Sutton ◽  
Andrew Booth ◽  
Pippa Evans
Keyword(s):  
New Zealand ◽  
Continuing Education ◽  
Question Answering ◽  
Information Science ◽  
Small Scale ◽  
Library And Information Science ◽  
Evidence Based ◽  
E Learning ◽  
Information Professionals ◽  
Information Practice

Objective – The project sought to examine the aspects of the question answering process in an evidence based library and information practice (EBLIP) context by presenting the questions asked, articles selected, and checklists used by an opportunistic sample of Australian and New Zealand library and information professionals from multiple library and information sectors participating in the “Evidence Based Library and Information Practice: Delivering Services That Shine” (EBLIP-Gloss) FOLIOz e-learning course. Methods – The researchers analyzed the “ask,” “acquire,” and “appraise” tasks completed by twenty-nine library and information professionals working in Australia or New Zealand. Questions were categorized by EBLIP domain, articles were examined to identify any comparisons, and checklists were collated by frequency. Results – Questions fell within each of the six EBLIP domains, with management being the most common. Timeliness, relevance, and accessibility were stronger determinants of article selection than rigour or study design. Relevance, domain, and applicability were the key determinants in selecting a checklist. Conclusion – This small-scale study exemplifies the EBLIP process for a self-selecting group of library and information professionals working in Australia and New Zealand. It provides a snapshot of the types of questions that library and information practitioners ask, and the types of articles and checklists found to be useful. Participants demonstrated a preference for literature and checklists originating from within the library and information science (LIS) field, reinforcing the imperative for LIS professionals to contribute to EBLIP research.

Bridging Inference Based Sentence Linking Model for Semantic Coherence

2016 ◽  
Vol 10 (1) ◽  
pp. 32-54
Author(s):  
Weidong Liu ◽  
Xiangfeng Luo ◽  
Jun Shu ◽  
Dandan Jiang
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Explicit Knowledge ◽  
Human Beings ◽  
Short Text ◽  
Weak Associate ◽  
Semantic Coherence ◽  
E Learning ◽  
Implicit And Explicit ◽  
Bridging Inference

As the various social Medias emerge on the web, how to link the large scale of unordered short texts with semantic coherence is becoming a practical problem since these short texts have vast decentralized topics, weak associate relations, abundant noise and large redundancy. The challenging issues to solve the above problem includes what knowledge foundation supports sentence linking process and how to link these unordered short texts for pursuing well coherence. Herein, the authors develop bridging inference based sentence linking model by simulating human beings' discourse bridging process, which narrows semantic coherence gaps between short texts. Such model supports linking process by implicit and explicit knowledge and proposes different bridging inference schemas to guide the linking process. The bridging inference based linking process under different schemas generates different semantic coherence including central semantics, concise semantics and layered semantics etc. To validate the bridging inference based sentence linking model, the authors conduct some experiments. Experimental results confirm that the proposed bridging inference based sentence linking process increases semantic coherence. The model can be used in short-text origination, e-learning, e-science, web semantic search, and online question-answering system in the future works.

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