371 research outputs found
Setting The Pace: Examining Cognitive Processing in MOOC Discussion Forums With Automatic Text Analysis
Learning analytics focuses on extracting meaning from large amounts of data. One of the largest datasets in education comes from Massive Open Online Courses (MOOCs) that typically feature enrollments in the tens of thousands. Analyzing MOOC discussion forums presents logistical issues, resulting chiefly from the size of the dataset, which can create challenges for understanding and adequately describing student behaviors. Utilizing automatic text analysis, this study built a hierarchical linear model that examines the influence of the pacing condition of a massive open online course (MOOC), whether it is self-paced or instructor-paced, on the demonstration of cognitive processing in a HarvardX MOOC. The analysis of 2,423 discussion posts generated by 671 students revealed the number of dictionary words used were positively associated with cognitive processing while analytical thinking and clout was negatively associated. We found that none of the student background information (gender, education), status of the course engagement (explored or completed), or the course pace (self-paced versus instructor paced) significantly influenced the cognitive processing of the postings
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Examining learnersâ social presence in a Massive Open Online Course through social network analysis and machine learning
Low engagement has been a longstanding problem in Massive Open Online Courses (MOOCs). However, engagement is crucial in social learning contexts to increase knowledge construction and achieve meaningful learning outcome. To further understand learnersâ engagement in MOOC discussion forums, this study focuses on the perspective of social presence, which is defined as learnersâ ability to project themselves socially and emotionally in a community of inquiry. Social presence is an important factor that has the potential to affect learnersâ learning experience and outcome. This study took place in the context of a professional development MOOC in the field of journalism. The discussion posts, system log data and survey responses were collected and analyzed. The purpose of this study is to understand the learnersâ participation patterns in the discussion forums over the six modules of the MOOC, and the relationship between learnersâ social presence, their positions in the learner network and their learning outcomes.
In terms of data analysis, this study adopted a mixed-method approach to examine the data from both qualitative and quantitative aspects: to qualitatively analyze the posts, a machine learning supported text classification model was developed and applied to automatically analyze the large-scale text data in the forums; social network analysis (SNA) was used to analyze the characteristics of the learner network and determine learnersâ centrality (degree, closeness, betweenness and Eigen centrality). Centrality is an important measure because prior studies found it to be an important predictor of learning outcome. Correlation analyses were used to discern the relationship between social presence and learnersâ centrality, while regression models were built to investigate how learnersâ social presence and posting behaviors (frequency of posting, average length of posts and day of posting) predict learnersâ network centrality. Finally, correlation analyses were conducted to understand the association between learnersâ network centrality and their certificate status, perceived learning and satisfaction. The purpose of using mixed methods is to see in what ways the qualitative nature of the posts and learnersâ posting behaviors impact learnersâ positions and influence in the learning community and their learning outcomes.
The findings revealed the evolvement of the learner network in relation to the distribution of social presence throughout the MOOC. The results also showed that social presence indicators such as Complimenting others, Expressing agreement, Expressing gratitude and Disagreement/doubts/criticism play important roles in learnersâ centrality in the learner network. Beside social presence, frequency of posting has strong effect in predicting learnersâ network centrality, while other factors such as the average length of posts and the timing of posting have marginal impact in the prediction. Finally, this study found that learnersâ network centrality is correlated with their certificate status as well as their overall satisfaction with the MOOC, but not correlated with their perceived learning in the MOOC. This study is among the first efforts in MOOC research to examine the relationship between social presence, learnersâ network centrality and learning outcomes. It provides a critical ground for studying content-related interaction and learning community in MOOC forums. The findings inform MOOC learners in terms of how to strategically present themselves in the discussion forums to increase the possibilities of peer interaction and achieve productive learning outcomes. For examples, findings suggest that learners may obtain more central position in the community by posting more compliments, expressing more gratitude, and communicating agreement and disagreement, doubts etc. While for MOOC instructors, this study will potentially inform them how to effectively mediate the discussions and improve learner engagement as a facilitator, such as paying attention to the changes of learner network, identifying central learners, monitoring learnersâ affective states.Curriculum and Instructio
Changes in Cost Incurred by Indonesian Teachers for Online Training during Covid-19 Pandemic
Due to Covid-19 transmission, the educational facilities in Indonesia were closed and teachers had to work from home (WFH). It caused face-to-face learning turn into online learning and online training. This study aimed to identify costs incurred by Indonesian teachers for online training during the Covid-19 pandemic. Data collection was carried out by distributing online questionnaires in Google forms to all teachers participating in the online training. Incoming responses were analyzed using SPSS version 26. The results explained that by having self- isolation at home, teachers used their free time to attend online learning. However, they had to pay extra to buy good internet services to properly attend the training. Before the Covid-19 transmission period, the highest internet cost per month was IDR 0-100,000. While during the Covid-19 pandemic, the highest internet expense per month was IDR 100,000-200,000. It was described by the number of respondents who reached 306 respondents (33.85%)
Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education
This paper presents a novel framework, Artificial Intelligence-Enabled
Intelligent Assistant (AIIA), for personalized and adaptive learning in higher
education. The AIIA system leverages advanced AI and Natural Language
Processing (NLP) techniques to create an interactive and engaging learning
platform. This platform is engineered to reduce cognitive load on learners by
providing easy access to information, facilitating knowledge assessment, and
delivering personalized learning support tailored to individual needs and
learning styles. The AIIA's capabilities include understanding and responding
to student inquiries, generating quizzes and flashcards, and offering
personalized learning pathways. The research findings have the potential to
significantly impact the design, implementation, and evaluation of AI-enabled
Virtual Teaching Assistants (VTAs) in higher education, informing the
development of innovative educational tools that can enhance student learning
outcomes, engagement, and satisfaction. The paper presents the methodology,
system architecture, intelligent services, and integration with Learning
Management Systems (LMSs) while discussing the challenges, limitations, and
future directions for the development of AI-enabled intelligent assistants in
education.Comment: 29 pages, 10 figures, 9659 word
Learning Representations of Social Media Users
User representations are routinely used in recommendation systems by platform
developers, targeted advertisements by marketers, and by public policy
researchers to gauge public opinion across demographic groups. Computer
scientists consider the problem of inferring user representations more
abstractly; how does one extract a stable user representation - effective for
many downstream tasks - from a medium as noisy and complicated as social media?
The quality of a user representation is ultimately task-dependent (e.g. does
it improve classifier performance, make more accurate recommendations in a
recommendation system) but there are proxies that are less sensitive to the
specific task. Is the representation predictive of latent properties such as a
person's demographic features, socioeconomic class, or mental health state? Is
it predictive of the user's future behavior?
In this thesis, we begin by showing how user representations can be learned
from multiple types of user behavior on social media. We apply several
extensions of generalized canonical correlation analysis to learn these
representations and evaluate them at three tasks: predicting future hashtag
mentions, friending behavior, and demographic features. We then show how user
features can be employed as distant supervision to improve topic model fit.
Finally, we show how user features can be integrated into and improve existing
classifiers in the multitask learning framework. We treat user representations
- ground truth gender and mental health features - as auxiliary tasks to
improve mental health state prediction. We also use distributed user
representations learned in the first chapter to improve tweet-level stance
classifiers, showing that distant user information can inform classification
tasks at the granularity of a single message.Comment: PhD thesi
Learning Representations of Social Media Users
User representations are routinely used in recommendation systems by platform
developers, targeted advertisements by marketers, and by public policy
researchers to gauge public opinion across demographic groups. Computer
scientists consider the problem of inferring user representations more
abstractly; how does one extract a stable user representation - effective for
many downstream tasks - from a medium as noisy and complicated as social media?
The quality of a user representation is ultimately task-dependent (e.g. does
it improve classifier performance, make more accurate recommendations in a
recommendation system) but there are proxies that are less sensitive to the
specific task. Is the representation predictive of latent properties such as a
person's demographic features, socioeconomic class, or mental health state? Is
it predictive of the user's future behavior?
In this thesis, we begin by showing how user representations can be learned
from multiple types of user behavior on social media. We apply several
extensions of generalized canonical correlation analysis to learn these
representations and evaluate them at three tasks: predicting future hashtag
mentions, friending behavior, and demographic features. We then show how user
features can be employed as distant supervision to improve topic model fit.
Finally, we show how user features can be integrated into and improve existing
classifiers in the multitask learning framework. We treat user representations
- ground truth gender and mental health features - as auxiliary tasks to
improve mental health state prediction. We also use distributed user
representations learned in the first chapter to improve tweet-level stance
classifiers, showing that distant user information can inform classification
tasks at the granularity of a single message.Comment: PhD thesi
ChatGPT and Beyond: The Generative AI Revolution in Education
The wide adoption and usage of generative artificial intelligence (AI)
models, particularly ChatGPT, has sparked a surge in research exploring their
potential applications in the educational landscape. This survey examines
academic literature published between November, 2022, and July, 2023,
specifically targeting high-impact research from Scopus-indexed Q1 and Q2
journals. This survey delves into the practical applications and implications
of generative AI models across a diverse range of educational contexts. Through
a comprehensive and rigorous evaluation of recent academic literature, this
survey seeks to illuminate the evolving role of generative AI models,
particularly ChatGPT, in education. By shedding light on the potential
benefits, challenges, and emerging trends in this dynamic field, the survey
endeavors to contribute to the understanding of the nexus between artificial
intelligence and education. The findings of this review will empower educators,
researchers, and policymakers to make informed decisions about the integration
of AI technologies into learning environments
Unraveling the Relationship between Content Design and Kinesthetic Learning on Communities of Practice Platforms
As a variant of the sharing economy, Communities of Practice (CoP) platforms have allowed kinesthetic learners to acquire skillsets corresponding to their interests for immediate or future use in practice. However, the impact of digital learning content design on kinesthetic learning remains underexplored in the field of information systems. We hence extend prior research by advancing content richness and structure clarity as antecedents affecting kinesthetic learnersâ digestibility of contents, culminating in differential kinesthetic learning effects. To substantiate our arguments, we collected data from a leading Chinese recipe sharing platform. Whereas content richness was measured in terms of readability, verb richness, and prototypicality, structure clarity was operationalized as block structure, block quantity, and block regularity. Employing a machine learning model, we simulated and tested learnersâ digestibility of image content embodied within recipes. Plans for future research beyond the current study are also discussed
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Automating Feedback to Improve Teachersâ Effective Use of Instructional Discourse in K-12 Mathematics Classrooms
Over the past decade, robust literature focused on teacher “talk moves” that promote student argumentation has emerged, especially in mathematics education. Teachers and students can use talk moves to construct conversations in which students share their thinking, actively consider the ideas of others, and engage in sustained reasoning. Providing teachers with detailed feedback about the talk moves utilized in their lessons requires considerable human expertise. These highly trained observers must hand-code transcripts of classroom recordings, analyze talk moves and provide one-on-one expert coaching, a time-consuming and expensive process. Our research team developed Talkback - an innovative application to address a significant challenge in education: providing teachers with immediate and actionable feedback on their use of effective classroom discourse strategies. My work is situated in the research and development of a cyberinfrastructure for TalkBack, including deep learning models for Natural Language Processing (NLP) for automated feedback. Starting with a bidirectional long short-term memory (bi-LSTM) network, I explore different state-of-the-art deep learning models, including transformers, to automatically analyze classroom recordings and generate information about classroom discourse strategies with F1 measures up to 78.92%. The TalkMoves dataset used for training and evaluating these models was curated by an interdisciplinary research team and comprised 500+ human-annotated classroom transcripts. The strong performance of both the student and the teacher talk moves models illustrates the reliability and robustness of artificial intelligence algorithms applied to noisy real-world classroom data. TalkBack application serves as an example to support a well-specified theory of learning (accountable talk), addresses a recognized challenge in education (teacher feedback), and has the potential to scale to large classrooms and teachers. The ability to better understand teachers’ perceptions and use of the TalkBack application can provide structured professional learning opportunities that promote discourse-rich pedagogy. Results from a mixed-methods study with teachers highlight several emergent themes relating to the perceived utility of TalkBack as an AI-based tool and serving as a platform for research and innovations in NLP and education.</p
Empathy Detection Using Machine Learning on Text, Audiovisual, Audio or Physiological Signals
Empathy is a social skill that indicates an individual's ability to
understand others. Over the past few years, empathy has drawn attention from
various disciplines, including but not limited to Affective Computing,
Cognitive Science and Psychology. Empathy is a context-dependent term; thus,
detecting or recognising empathy has potential applications in society,
healthcare and education. Despite being a broad and overlapping topic, the
avenue of empathy detection studies leveraging Machine Learning remains
underexplored from a holistic literature perspective. To this end, we
systematically collect and screen 801 papers from 10 well-known databases and
analyse the selected 54 papers. We group the papers based on input modalities
of empathy detection systems, i.e., text, audiovisual, audio and physiological
signals. We examine modality-specific pre-processing and network architecture
design protocols, popular dataset descriptions and availability details, and
evaluation protocols. We further discuss the potential applications, deployment
challenges and research gaps in the Affective Computing-based empathy domain,
which can facilitate new avenues of exploration. We believe that our work is a
stepping stone to developing a privacy-preserving and unbiased empathic system
inclusive of culture, diversity and multilingualism that can be deployed in
practice to enhance the overall well-being of human life
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