With the proliferation of technology, the field of e-learning has garnered significant attention in recent times This is because it has allowed users from around the world to learn and access new information This has added to the growing amount of collected data that is already being generated through different devices and sensors employed around the world This has led to the need to analyze collected data and extract useful information from it Machine learning (ML) and data analytics (DA) are proposed techniques that can help extract information and find valuable patterns within the collected data In this paper, the field of e-learning is investigated in terms of definitions and characteristics Moreover, the various challenges facing the different participants within this process are discussed In addition, some of the works proposed in the literature to tackle these challenges are presented Then, a brief survey about some of the most popular ML and DA techniques is given Finally, some of the research opportunities available that employ such techniques are proposed to give insights into the areas that merit further exploration and investigation

E-Learning: Challenges and Research Opportunities Using Machine Learning & Data Analytics

The state-of-the-art in expert recommendation systems

This survey presents an overview of information retrieval, natural languageprocessing and machine learning research that makes use of forumdata, including both discussion forums and community questionansweringcQA archives. The focus is on automated analysis, withthe goal of gaining a better understanding of the data and its users.We discuss the different strategies used for both retrieval taskspost retrieval, question retrieval, and answer retrieval and classificationtasks post type classification, question classification, post qualityassessment, subjectivity, and viewpoint classification at the postlevel, as well as at the thread level thread retrieval, solvedness andtask orientation, discourse structure recovery and dialogue act tagging,QA-pair extraction, and thread summarisation. We also review workon forum users, including user satisfaction, expert finding, questionrecommendation and routing, and community analysis.The survey includes a brief history of forums, an overview of thedifferent kinds of forums, a summary of publicly available datasets forforum research, and a short discussion on the evaluation of retrievaltasks using forum data.The aim is to give a broad overview of the different kinds of forumresearch, a summary of the methods that have been applied, some insightsinto successful strategies, and potential areas for future research.

/pdf/web-forum-retrieval-and-text-analytics-a-survey-3wsatlwomc.pdf

Web Forum Retrieval and Text Analytics: A Survey

This study aims to know the e-learning modality that stimulates students' learning process according to twenty-two English teachers in Santander, Colombia, and the correlation between learners' motivation and the challenges and opportunities in an online EFL class. To carry out this study, a mixed research approach was applied. To gather such information, a questionnaire was sent to the participants via email. Once data was scrutinized, it was found that this group of English teachers view synchronous encounters and access to information regardless of the geographical position as elements that can positively impact the students' motivation levels. Furthermore, this sample stated their role is significant, especially when mediating online lessons.

https://journals.umt.edu.pk/index.php/llr/article/download/963/500

Online Teaching During Covid-19: How to Maintain Students Motivated in an EFL Class

Question classification is a key point in many applications, such as Question Answering (QA, e.g., Yahoo! Answers), Information Retrieval (IR, e.g., Google search engine), and E-learning systems (e.g., Bloom's tax. classifiers). This paper aims to carry out a systematic review of the literature on automatic question classifiers and the technology directly involved. Automatic classifiers are responsible for labeling a certain evaluation item using a type of categorization as a selection criterion. The analysis of 80 primary studies previously selected revealed that SVM is the main algorithm of the Machine Learning used, while BOW and TF-IDF are the main techniques for feature extraction and selection, respectively. According to the analysis, the taxonomies proposed by Li and Roth and Bloom were the most used ones for the classification criteria, and Accuracy/Precision/Recall/F1-score were proven to be the most used metrics. In the future, the objective is to perform a meta-analysis with the studies that authorize the availability of their data.

Automatic Question Classifiers: A Systematic Review

Expert finding can be required for a variety of purposes: finding referees for a conference paper, recommending consultants for a software project, and identifying qualified answerers for a question in online knowledge-sharing communities, to name a few. This paper presents taxonomy of the task of expert finding that highlights the differences between finding experts, from the type of expertise indicator’s point of view. The taxonomy supports deep understanding of different sources of expertise information in the enterprise or online communities; for example, authored documents, emails, online posts, and social networks. In addition, different content and non-content features that characterize the evidence of expertise are discussed. The goal is to guide researchers who seek to conduct studies regarding the different types of expertise indicators and state-of-the-art techniques for expert finding in organizations or online communities. The paper concludes that although researchers have utilized a large number of graph and machine-learning techniques for locating expertise, there are still technical issues associated with the implementation of some of these methods. It also corroborates that combining content-based expertise indicators and social relationships has the benefit of alleviating some of the issues related to identifying and ranking answer experts. The above findings give implications for developing new techniques for expert finding that can overcome the technical issues associated with the performance of current methods.

Understanding expert finding systems: domains and techniques

Synchronous and Asynchronous are two distinct modes or platforms of e-learning that have been identified in various works. In this work, asynchronous platform is realised on Modular Object Oriented Dynamic Learning Environment (MOODLE) whilethe synchronous platform is examined on Elluminate.The tools and features of available technological resources to implement these learning platforms are examined and compared. The features of these resources were studied with emphasis on their back-end engine as well as the front-end engine for the purpose of effective system implementation. This involves the analysis of design requirements such as operating system, web services, hypertext processor, server and database platform. The comparative study is based on the Microsoft Windows operating system platform.

/pdf/a-comparative-study-of-synchronous-and-asynchronouse-3lanalbyf8.pdf

A comparative study of synchronous and asynchronouse-learning resources

Background/Objective:A web forum is a problem-solving online community.Web forum research activitieshave been focused on answer mining with the assumption that the starting post is a question post. This paper proposes methods for mining standard web forum questions. Methods/Statistical Analysis:Popular methods for web forum question post detection are question mark, question words, higher n-grams and sequential pattern mining. These methods have problem of low detection rate and implementation complexity. Implemented in this paper is hybridization of simple bag-of-words model with web forum metadata, simple rule of question mark and question words. Dimensional reduction was performed using chi-square and wrapper techniques. Findings:The quality of web forum question posts varies from excellent to mediocre or even spam. Detecting good question posts is non-trivial. It requires utilization of salient features. Combination of simple rule of question mark and question words with forum metadata performed better than each of the two.Integration of bag-of-words model with simple rule of question marks, question words and forum metadata enhances question post detection. Dimensionality reduction using chi-square were found to perform better than other popular filters like info gain, gain ratio and symmetric uncertain. Applications/Improvements: Three publicly available datasets of varying technical degrees were used for the experiments.The experimental results revealed that an enhanced bag-of-words model can perform better than complex techniques that implement higher N-gram with part-of-speech tagging.

Hybridization of Bag-of-Words and Forum Metadata for Web Forum Question Post Detection

Frequently Asked Questions (FAQ)'s tag is becoming more popular on websites. Research activities have been concentrated on its retrieval rather than construction. FAQ construction can be achieved using a number of sources. Presently, it is mostly done manually by help desk staff and this tends to make it static in nature. In this paper, a comprehensive review of various components that can guarantee effective mining of FAQ from forum threads is presented. The components encompass pre-processing, mining of questions, mining of answers and mining of the FAQ. Besides the general idea and concept, we discuss the strengths and limitations of the various techniques used in these components. In fact, the following questions are addressed in the review. What kind of pre-processing technique is needed for mining FAQ from forum? What are the recent techniques for mining questions from forum threads? What approaches are currently dominating answer retrieval from forum threads? How can we cluster out FAQ from question and answer database?.

Mining FAQ from forum threads: theoretical framework

A Web forum is an online community that connects people with common interest together Within the forum, members interact to share knowledge, expertise and resources A major issue in detecting web forum answers is to establish a good relationship between the question and the candidate answer This relationship is often established using lexical features Web forum text, unlike news articles, is faced with noise challenges, and this hinders the performance of lexical features In this paper, we investigate the effect of noise on most of the common lexical features used in mining web forum answers with a view of normalizing it to enhance the performance of the features We propose 13 lexical features for exploration These features belong to four different quality dimensions that can guarantee good answers We empirically address the following questions in the paper What category of noise is more rampant in web forum? What lexical mining features are more susceptible to noise? Will normalization of forum corpus enhance the performance of lexical features in detecting web forum answers? We used three publicly available datasets of varying technical degrees for the experiments The experimental results revealed that proper normalization of web forum corpora can yield up to 9% increase in the performance of the lexical features

http://ieeexplore.ieee.org/iel7/7310650/7322996/07323035.pdf

Adekunle Isiaka Obasa

Papers

Understanding expert finding systems: domains and techniques

A comparative study of synchronous and asynchronouse-learning resources

Hybridization of Bag-of-Words and Forum Metadata for Web Forum Question Post Detection

Mining FAQ from forum threads: theoretical framework

Enhanced lexicon based model for web forum answer detection