Social media big data analytics: A survey

AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages of natural language tutoring are presented. Next, we review three central themes in AutoTutor’s development: human-inspired tutoring strategies, pedagogical agents, and technologies that support natural-language tutoring. Research on early versions of AutoTutor documented the impact on deep learning by co-constructed explanations, feedback, conversational scaffolding, and subject matter content. Systems that evolved from AutoTutor added additional components that have been evaluated with respect to learning and motivation. The latter findings include the effectiveness of deep reasoning questions for tutoring multiple domains, of adapting to the affect of low-knowledge learners, of content over surface features such as voices and persona of animated agents, and of alternative tutoring strategies such as collaborative lecturing and vicarious tutoring demonstrations. The paper also considers advances in pedagogical agent roles (such as trialogs) and in tutoring technologies, such semantic processing and tutoring delivery platforms. This paper summarizes and integrates significant findings produced by studies using AutoTutor and related systems.

/pdf/autotutor-and-family-a-review-of-17-years-of-natural-5d8pg93iag.pdf

AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring

Smoothing Forecasting And Prediction Of Discrete Time Series

https://files.eric.ed.gov/fulltext/ED586834.pdf

AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring.

Mathematics is fundamental for many professions, especially science, technology, and engineering. Yet, mathematics is often perceived as difficult and many students leave disciplines in science, technology, engineering, and mathematics (STEM) as a result, closing doors to scientific, engineering, and technological careers. In this editorial, we argue that how mathematics is traditionally viewed as “given” or “fixed” for students’ expected acquisition alienates many students and needs to be problematized. We propose an alternative approach to changes in mathematics education and show how the alternative also applies to STEM education.

/pdf/problematizing-teaching-and-learning-mathematics-as-given-in-5g5wbt3623.pdf

Problematizing teaching and learning mathematics as “given” in STEM education

This study investigated learning outcomes and user perceptions from interactions with a hybrid intelligent tutoring system created by combining the AutoTutor conversational tutoring system with the Assessment and Learning in Knowledge Spaces (ALEKS) adaptive learning system for mathematics. This hybrid intelligent tutoring system (ITS) uses a service-oriented architecture to combine these two web-based systems. Self-explanation tutoring dialogs were used to talk students through step-by-step worked examples to algebra problems. These worked examples presented an isomorphic problem to the preceding algebra problem that the student could not solve in the adaptive learning system. Due to crossover issues between conditions, experimental versus control condition assignment did not show significant differences in learning gains. However, strong dose-dependent learning gains were observed that could not be otherwise explained by either initial mastery or time-on-task. User perceptions of the dialog-based tutoring were mixed, and survey results indicate that this may be due to the pacing of dialog-based tutoring using voice, students judging the agents based on their own performance (i.e., the quality of their answers to agent questions), and the students’ expectations about mathematics pedagogy (i.e., expecting to solving problems rather than talking about concepts). Across all users, learning was most strongly influenced by time spent studying, which correlated with students’ self-reported tendencies toward effort avoidance, effective study habits, and beliefs about their ability to improve in mathematics with effort. Integrating multiple adaptive tutoring systems with complementary strengths shows some potential to improve learning. However, managing learner expectations during transitions between systems remains an open research area. Finally, while personalized adaptation can improve learning efficiency, effort and time-on-task for learning remains a dominant factor that must be considered by interventions.

/pdf/skope-it-shareable-knowledge-objects-as-portable-intelligent-1d1vqd3ly7.pdf

SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): overlaying natural language tutoring on an adaptive learning system for mathematics

The increased use of cyber-enabled systems and Internet-of-Things (IoT) led to a massive amount of data with different structures. Most big data solutions are built on top of the Hadoop eco-system or use its distributed file system (HDFS). However, studies have shown inefficiency in such systems when dealing with today's data. Some research overcame these problems for specific types of graph data, but today's data are more than one type of data. Such efficiency issues may lead to large-scale problems, including larger space requirements in data centers, and waste in resources (like power consumption), that in turn lead to environmental problems (such as more carbon emission) [1] , as per scholars. We propose a data-aware module for the Hadoop eco-system. We also propose a distributed encoding technique for genetic algorithms efficient data processing. Our framework allows Hadoop to manage the distribution of data and its placement based on cluster analysis of the data itself. We are able to handle a broad range of data types as well as optimize query time and resource usage. We performed experiments on multiple datasets generated via LUBM (Lehigh University Benchmark) and reported results along with performance analysis.

Handling Big Data Using a Data-Aware HDFS and Evolutionary Clustering Technique

Natural language tutoring systems generate significant data during their tutoring sessions, which is often not used to inform real-time, persistent student models. The current research explores the feasibility of mapping concept-focused tutoring sessions to knowledge components, by breaking sessions down into features that are integrated into a session score. Three classes of tutoring conversation features were studied: semantic match of student contributions to domain content, tutor support (e.g., hints and prompts), and student verbosity (i.e., word counts). Analysis of the relative importance of these features and the ability of these features to predict later task performance on similar topics was conducted. Reinforcing prior work, semantic match scores were a key predictor for later test performance. Tutor help features (hints, prompts) were also useful secondary predictors. Unlike some related work, verbosity was a key predictor even after accounting for the semantic match.

Exploring real-time student models based on natural-language tutoring sessions.

In this article we describe a framework (DEGA-Gen) for the application of distributed genetic algorithms for detection of communities in networks. The framework proposes efficient ways of encoding the network in the chromosomes, greatly optimizing the memory use and computations, resulting in a scalable framework. Different objective functions may be used for producing division of network into communities. The framework is implemented using open source implementation of MapReduce paradigm, Hadoop. We validate the framework by developing community detection algorithm, which uses modularity as measure of the division. Result of the algorithm is the network, partitioned into non-overlapping communities, in such a way, that network modularity is maximized. We apply the algorithm to well-known data sets, such as Zachary Karate club, bottlenose Dolphins network, College football dataset, and US political books dataset. Framework shows comparable results in achieved modularity; however, much less space is used for network representation in memory. Further, the framework is scalable and can deal with large graphs as it was tested on a larger youtube.com dataset.

Distributed evolutionary approach to data clustering and modeling

Clustering algorithms have emerged as a powerful learning tool to accurately analyze the massive amount of data generated by current applications and smart technologies. Precisely, their main objective is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a wide and diverse body of knowledge in the area of clustering and there has been attempts apply these algorithms and scale it to adopt todays data. However, one major challenge in using clustering algorithms is scalability of such algorithms in a way that faces the challenges and computational cost of clustering big data. In this paper, we are describing a mapping between graph clustering problem and data clustering. Using genetic algorithms and multi-objective optimization as well as distributed graph stores, the proposed algorithm (1) transform big data into Distributed RDF graphs. With (2) a novel distributed encoding techniques. The algorithm (3) scales to deal with big RDF graphs to (4) produce clusters by maximizing graph modularity as a main objective. The results on LUBM generated big data shows the (5) ability to deal with the challenges provided such data and (6) produce comparative results compared to other peers of clustering algorithms

Mustafa H. Hajeer

Papers

SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): overlaying natural language tutoring on an adaptive learning system for mathematics

Handling Big Data Using a Data-Aware HDFS and Evolutionary Clustering Technique

Exploring real-time student models based on natural-language tutoring sessions.

Distributed evolutionary approach to data clustering and modeling

Distributed genetic algorithm to big data clustering