This dataset captures simulated student interaction data within a virtual learning environment (VLE), focusing on behavioral indicators related to academic engagement. It includes a variety of features that reflect how students participate in online courses, such as time spent on the platform, quiz scores, forum activity, and content completion.

Each entry is labeled with an engagement level—Low, Medium, or High—based on aggregated interaction metrics. The dataset supports exploratory analysis and the development of data-driven strategies to understand and improve student engagement in virtual settings.

🔑 Key Features: Time Spent Weekly: Average number of minutes a student spends on the platform.

Quiz Score Average: Mean score across online assessments.

Forum Posts: Number of contributions to discussion forums.

Video Watched Percent: Percentage of course video content completed.

Assignments Submitted: Count of assignments submitted on time.

Login Frequency: Number of logins per week.

Session Duration Average: Average duration per platform session.

Device Type: Platform used to access the content (e.g., Desktop, Mobile).

Course Difficulty: Self-reported or platform-defined difficulty of enrolled courses.

Region: Geographic classification (Urban, Suburban, Rural).

Engagement Level: Categorical label indicating Low, Medium, or High engagement.

This dataset can assist educators, researchers, and learning platform designers in understanding key behavioral patterns that influence student participation and success in online learning environments.

Please cite the following paper when using this dataset:

N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109

Abstract

The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations, centered around information seeking and sharing, related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description

The dataset comprises a total of 52,984 Tweet IDs (that correspond to the same number of Tweets) about online learning that were posted on Twitter from 9th November 2021 to 13th July 2022. The earliest date was selected as 9th November 2021, as the Omicron variant was detected for the first time in a sample that was collected on this date. 13th July 2022 was the most recent date as per the time of data collection and publication of this dataset.

The dataset consists of 9 .txt files. An overview of these dataset files along with the number of Tweet IDs and the date range of the associated tweets is as follows. Table 1 shows the list of all the synonyms or terms that were used for the dataset development.

Filename: TweetIDs_November_2021.txt (No. of Tweet IDs: 1283, Date Range of the associated Tweet IDs: November 1, 2021 to November 30, 2021)

Filename: TweetIDs_December_2021.txt (No. of Tweet IDs: 10545, Date Range of the associated Tweet IDs: December 1, 2021 to December 31, 2021)

Filename: TweetIDs_January_2022.txt (No. of Tweet IDs: 23078, Date Range of the associated Tweet IDs: January 1, 2022 to January 31, 2022)

Filename: TweetIDs_February_2022.txt (No. of Tweet IDs: 4751, Date Range of the associated Tweet IDs: February 1, 2022 to February 28, 2022)

Filename: TweetIDs_March_2022.txt (No. of Tweet IDs: 3434, Date Range of the associated Tweet IDs: March 1, 2022 to March 31, 2022)

Filename: TweetIDs_April_2022.txt (No. of Tweet IDs: 3355, Date Range of the associated Tweet IDs: April 1, 2022 to April 30, 2022)

Filename: TweetIDs_May_2022.txt (No. of Tweet IDs: 3120, Date Range of the associated Tweet IDs: May 1, 2022 to May 31, 2022)

Filename: TweetIDs_June_2022.txt (No. of Tweet IDs: 2361, Date Range of the associated Tweet IDs: June 1, 2022 to June 30, 2022)

Filename: TweetIDs_July_2022.txt (No. of Tweet IDs: 1057, Date Range of the associated Tweet IDs: July 1, 2022 to July 13, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (link to download and a step-by-step tutorial on how to use Hydrator) may be used.

Table 1. List of commonly used synonyms, terms, and phrases for online learning and COVID-19 that were used for the dataset development

Terminology

List of synonyms and terms

COVID-19

Omicron, COVID, COVID19, coronavirus, coronaviruspandemic, COVID-19, corona, coronaoutbreak, omicron variant, SARS CoV-2, corona virus

online learning

online education, online learning, remote education, remote learning, e-learning, elearning, distance learning, distance education, virtual learning, virtual education, online teaching, remote teaching, virtual teaching, online class, online classes, remote class, remote classes, distance class, distance classes, virtual class, virtual classes, online course, online courses, remote course, remote courses, distance course, distance courses, virtual course, virtual courses, online school, virtual school, remote school, online college, online university, virtual college, virtual university, remote college, remote university, online lecture, virtual lecture, remote lecture, online lectures, virtual lectures, remote lectures

This is the dataset "European remote e-learning ecosystem survey data" of the e-DIPLOMA project.

In 2022 the evaluation of the European tertiary training ecosystem capacity for using disruptive technologies in practice based e-learning was explored. It was done in the eDiploma project WP2. The research problem was: What are the main gaps in tertiary education in the institutional capacity to perform practice based e-learning with disruptive technologies? Three survey instruments were developed for three target groups in institutions: technology specialists, educators and students. The survey was composed of four blocks of capacity elements:

• infrastructural capacities,

• normative and regulatory capacities (institutional level),

• teaching cultures (community level),

• competences, attitudes and values (personal level).

The data were collected with the anonymous web based survey approach in countries: Spain, Estonia, Hungary, Bulgaria, Italy, Cyprus.

In each HEI or VET institution the respondents were:

• Technical and didactical support staff: educational technologist, IT or technical support specialists, lecturers responsible for technology training, Digital policy administrative specialist

• Lecturers or researchers who have experiences with some forms of group-learning or practice based learning

• Students from the institution who have experiences with some forms of group-learning or practice based learning / to be spread among each institution, so that different areas students respond, these should not be one group from one class only)

The answers were collected totally from the following number of the technology specialists-experts (N=96), the educators (N=351), and the students (N=516). The generalizability of the data is limited due to the sampling structure: it was not attempted to reach regional coverage because countries in our sample differ greatly in size. In Estonia responses were collected from 9 institutions (3 vocational schools and 6 HEIs). In Bulgaria responses were from 3 institutions (all HEIs). In Cyprus responses were from 3 institutions (all HEIs). In Hungary responses were from 6 institutions (1 vocational school and 5 HEIs). In Spain responses were from 116 institutions (28 high schools, 41 vocational schools, 47 HEIs). In Italy responses were from 9 institutions (4 HEIs and 5 social enterprises).

Learning occurs best when students are given opportunities to be active participants in the learning process. As assessment strategies are being forced to change in the era of Generative AI, and as digital technologies continue to integrate with education, it becomes imperative to gather information on current approaches to evaluating student participation. This mini-review aimed to identify existing methods used by higher education teachers to assess participation in both physical and virtual classrooms. It also aimed to identify common issues that are anticipated to impact future developments in this area. To achieve these objectives, articles were downloaded from the ERIC database. The search phrase “assessment of class participation” was utilized. Search was limited to peer-reviewed articles written in English. The educational level was limited to “higher education” and “postsecondary education” in the search. From the 2,320 articles that came up, titles and abstracts were screened and 65 articles were retained. After reading the full text, a total of 45 articles remained for analysis, all published between 2005 and 2023. Using thematic analysis, the following categories were formed: innovations in assessing class participation, criteria-related issues, and issue of fairness in assessing class participation. As education becomes more reliant on technology, we need to be cognizant of issues that came up in this review regarding inequity of educational access and opportunity, and to develop solutions that would promote equitable learning. We therefore call for more equity-focused innovation, policymaking, and pedagogy for more inclusive classroom environments. More implications and potential directions for research are discussed.

Abstract The article presents the development and results of a systematic review of the literature on Mathematics Education and Distance Learning. This review is part of a doctoral research in development on e-learning and b-learning practices in Brazilian Mathematics Teacher Education Programs. The main objective of the review was to identify in Mathematics Education how previous researches (January 2011 and December 2017) defined the e-learning and b-learning teaching models. In addition, it is possible to understand at what levels of education these investigations are situated: basic education, initial or continuing teacher education. Although focusing on a doctoral undergraduate research, it is believed that the previous research, reproduced at other school levels, can also add elements and reflections to understand these models of courses in Distance Education. We carried out a systematic review based on orientations from different organizations and researchers dedicated to this area of research. In this sense, we followed different phases in the process to make the review: definition of objectives/questions, research equations and databases; determination of inclusion, exclusion, and methodological validity criteria; presentation and discussion of results; and data. As supporting software, both Google spreadsheets and NVivo11 were herein used. In addition to a higher incidence of work that occur in the teacher training context, the review results show great dispersion about the concept of e-learning and a lower occurrence of studies on b-learning models. Also, a significant number of works refer to the need to create conditions, in Distance Teacher Education Programs, for the constitution of (virtual) learning communities.

This dataset includes FAQ data and their categories to train a chatbot specialized for e-learning system used in Tokyo Metropolitan University. We report accuracies of the chatbot in the following paper.

Yasunobu Sumikawa, Masaaki Fujiyoshi, Hisashi Hatakeyama, and Masahiro Nagai "Supporting Creation of FAQ Dataset for E-learning Chatbot", Intelligent Decision Technologies, Smart Innovation, IDT'19, Springer, 2019, to appear.

Yasunobu Sumikawa, Masaaki Fujiyoshi, Hisashi Hatakeyama, and Masahiro Nagai "An FAQ Dataset for E-learning System Used on a Japanese University", Data in Brief, Elsevier, in press.

This dataset is based on real Q&A data about how to use the e-learning system asked by students and teachers who use it in practical classes. The duration we collected the Q&A data is from April 2015 to July 2018.

We attach an English version dataset translated from the Japanese dataset to ease understanding what contents our dataset has. Note here that we did not perform any evaluations on the English version dataset; there are no results how accurate chatbots responds to questions.

File contents:

• FAQ data (*.csv)
  1. Answer2Category.csv: Categories of answers.
  2. Answer2Tag.csv: Titles of answers.
  3. Answers.csv: IDs for answers and texts of answers.
  4. Categories.csv: Names of categories for answers.
  5. Questions.csv: Texts of questions and their corresponding answer IDs.
  6. Answers_english.csv: IDs for answers and texts of answers written in English.
  7. Categories_english.csv: Names of categories for answers and their corresponding English names.
  8. Questions_english.csv: Texts of questions and their corresponding answer IDs written in English.


• Statistics (*.tsv)

  Results of statistical analyses for the dataset. We used Calinski and Harabaz method, mutual information, Jaccard Index, TF-IDF+KL divergence, and TF-IDF+JS divergence in order to measure qualities of the dataset. In the analyses, we regard each answer as a cluster for questions. We also perform the same analyses for categories by regarding them as clusters for answers.

Grants: JSPS KAKENHI Grant Number 18H01057

The digitalization of university teaching has been taking place for many years and, in addition to traditional teaching formats such as practicals and face-to-face lectures, more and more e-learning courses have been used in veterinary education for several years. In the context of the COVID-19 pandemic, universities across Germany had to switch to an ad-hoc, purely digital summer semester. This study evaluated the experiences and implementation of the digital summer semester 2020 at the University of Veterinary Medicine Hannover (TiHo) Foundation from the perspective of the teaching staff. In addition to the technical equipment used by lecturers, this survey also focused on the effects of the digital semester on teaching and the future practicality of digital teaching formats and strategies in veterinary education. Therefore, a questionnaire was designed and distributed among lecturers involved in the digital summer semester 2020. One hundred and three completed questionnaires were evaluated. The results of the evaluation show that teachers see huge potential in blended learning as a teaching method in veterinary education. In addition, teachers were able to digitize teaching well with the available hardware and software. The teaching staff saw difficulties above all in the loss of practical training and in the digitalization of practical exercises. Teachers also needed significantly more time to plan and implement digital teaching compared to pure face-to-face teaching. In summary blended learning offers many advantages, such as increased flexibility for students and teaching staff. In order to be able to use digital teaching methods and strategies profitably in veterinary education in the future, well thought-out didactic concepts and further technical expansion of the universities are required. In addition, the digital skills of teaching staff should be further trained and promoted.

To understand the perceptions of families, students, and teachers regarding their school. School leaders use feedback from the survey to reflect and make improvements to schools and programs. Also, results from the survey used to help measure school quality. Each year, all parents, teachers, and students in grades 6-12 take the NYC School Survey. The survey is aligned to the DOE's Framework for Great Schools. It is designed to collect important information about each school's ability to support student success.

Please note: The larger complete data file is downloadable under the Attachments Section

One of the sectors that felt the impact of the Corona Virus Disease 2019 (COVID-19) pandemic was the educational sector. The outbreak led to the immediate closure of schools at all levels thereby sending billions of students away from their various institutions of learning. However, the shut down of academic institutions was not a total one as some institutions that were solely running online programmes were not affected. Those who were running face to face and online modes quickly switched over to the online mode. Unfortunately, institutions that have not fully embraced online mode of study were greatly affected. 85% of academic institutions in Nigeria are operating face to face mode of study, therefore, majority of Nigerian students at all levels were affected by the COVID-19 lockdown. Social media platforms and emerging technologies were the major backbones of institutions that are running online mode of study, therefore, this survey uses the unified theory of acceptance and use of technology (UTAUT) model to capture selected Face to face Nigerian University students accessibility, usage, intention and willingness to use these social media platforms and emerging technologies for learning. The challenges that could mar the usage of these technologies were also revealed. Eight hundred and fifty undergraduate students participated in the survey.

The dataset includes the questionnaire used to retrieve the data, the responses obtained in spreadsheet format, the charts generated from the responses received, the Statistical Package of the Social Sciences (SPSS) file and the descriptive statistics for all the variables captured. This second version contains the reliability statistics of the UTAUT variables using Cronbach's alpha. This measured the reliability as well as the internal consistency of the UTAUT variables. This was measured in terms of the reliability statistics, inter-item correlation matrix and item-total statistics. Authors believed that the dataset will enhance understanding of how face to face students use social media platforms and how these platforms could be used to engage the students outside their classroom activities. Also, the dataset exposes how familiar face to face University students are to these emerging teaching and learning technologies.

While this special pandemic period has been seeing an increasing use of blended learning, few studies have meta-analytically reviewed the effectiveness of blended learning in different countries. This meta-analysis summarizes previous studies on blended learning effectiveness in different countries in terms of students' performance, students' attitudes toward blended learning, learning achievement, and student engagement in different countries. Through the meta-analysis via Stata/MP 14.0, it is concluded that blended learning can improve performance, attitude, and achievement in most countries. However, in both China and the USA, blended learning cannot significantly improve student engagement in academic activities. No significant differences were revealed in student performance in the USA between blended and non-blended learning. Future research can extend the research into blended learning to more countries and areas across the world.

Since 1998, the New York City Police Department (NYPD) has been tasked with the collection and maintenance of crime data for incidents that occur in New York City public schools. The NYPD has provided this data to the New York City Department of Education (DOE). The DOE has compiled this data by schools and locations for the information of our parents and students, our teachers and staff, and the general public. In some instances, several Department of Education learning communities co-exist within a single building. In other instances, a single school has locations in several different buildings. In either of these instances, the data presented here is aggregated by building location rather than by school, since safety is always a building-wide issue. We use “consolidated locations” throughout the presentation of the data to indicate the numbers of incidents in buildings that include more than one learning community.

The Young Life and Times Survey (YLT) originally began as a companion survey to the Northern Ireland Life and Times Survey (NILT) series. It surveyed young people aged 12-17 living in the households of adults interviewed for NILT, and YLT ran alongside it from 1998-2000. Following an evaluation in 2001, the YLT series recommenced in 2003 (see SN 4826) using a completely different methodology and independent of the adult NILT. This new YLT survey uses Child Benefit records as a sampling frame.

The aims of the YLT series are to:

• monitor public attitudes towards social policy and political issues in Northern Ireland;
• provide a time series on attitudes to key social policy areas;
• facilitate academic social policy analysis; provide a freely available resource on public attitudes for the wider community of users in Northern Ireland;
• give a voice to young people.

An open access time-series teaching dataset has been created from the 2003-2012 YLTs - see SN 7548.

The Kids’ Life and Times (KLT) survey of P7 children (10-11 year olds) is also part of the same suite of surveys as YLT and NILT.

Further information about the YLT, including publications, may be found on the Access Research Knowledge (ARK) YLT webpages.

The Young Life and Times Survey, 2003-2012: Teaching Dataset is part of a suite of teaching and learning resources created as part of a Higher Education Academy (HEA) strategic project focusing on teaching research methods. The project Learning by numbers: new open educational resources for teaching quantitative methods involved the creation of new teaching datasets from two major surveys focusing on Northern Ireland, with accompanying 'student-friendly' documentation and teaching guidelines. Specifically, two teaching datasets were created using NILT 2012 (see SNs 7546 and 7547) as well as this time-series teaching dataset drawing on the YLT 2003-2012 surveys. Documentation combining an edited technical report and codebook accompanies the teaching datasets. This documentation includes details of all the variables included in the teaching datasets as well as a summary technical report, with the main issues outlines in accessible language, for example, research design, sampling and response rates. Teaching guidelines drawing upon the particular variables included in the datasets are also available.

This dataset is based on YLT 2003-2012, and adapted for the purposes of this project. Some variables have been constructed and/or simplified for this teaching dataset – notes are provided in the codebook. While the teaching datasets contains the same total number of respondents, they are intended for teaching purposes only; it is advisable to use the original YLT annual studies for research (see SNs 4826, 5175, 5338, 5674, 5818, 6274, 6531, 6820, 7058 and 7409). Further information about the teaching datasets may be found on the ARK Teaching datasets: Learning by numbers webpage.

Online Group Community Engagement

Learning Languages and Skills, Challenges, and Participation

By [source]

About this dataset

This dataset is invaluable in understanding how individuals engage in group environments and what learning outcomes come from it. It contains multi-dimensional data that includes indicators of a person's desire to learn, expertise, participation and engagement - all of which can be used to measure the impact of learning. With this data, we can understand how people are engaging within groups and their overall accomplishments from their interactions. The insights provided here can be used to better inform online group activities for better results

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Start by identifying the goal of your analysis. What specific questions are you looking to answer with this dataset, and what kind of insights or patterns can you expect to find?
• Filter out the data that is necessary for your research and understand the different columns in the dataset. This will help narrow down on specific points of analysis which can be seen from visualizing different metrics such as desire to learn, expertise, participation and engagement.
• Visualize different set of data using various tools such as graphs or tables so as to get an overall idea about how different metrics interact with each other in order to produce a learning outcome.

Research Ideas

• Assessing the effectiveness of online education platforms and courses. By analyzing individual participation, engagement and learning outcomes, educators can determine if their platform or specific course is engaging students successfully and helping them learn.
• Developing personalized learning plans for each student or group of students to ensure they are getting the most value out of their educational experiences. Analyzing data from this dataset can provide insight into which specific activities a student should focus on in order to best facilitate their learning journey.
• Understanding how different demographic groups interact within online group environments so that educators can develop targeted approaches towards those demographics, ensuring everyone receives an equitable educational experience regardless of gender, race, etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

These couple of years have witnessed an increase in interest in Higher Education Institutions (HEIs) have for Massive Open Online Courses (MOOCs). As the COVID-19 situation amply showed us, MOOCs promptly became a practical way to secure continuity of education for students in lockdown. Educational institutions chose the MOOC format to swiftly adapt to the “new normal” and deliver their courses online without incurring too many unbudgeted expenses. However, the quality of teaching practices and learning design in MOOCs’ Digital Learning Environments (DLEs) varies considerably. Also, while the interest in the MOOC format has increased, the emergent body of specific research on MOOCs for language learning or Language MOOCs (LMOOCs) is unfortunately still limited. By choosing a connectivist approach to understand teaching and learning dynamics in DLEs, this article will elaborate on the importance of learning design and Digital Story Telling (DST) to create sustainable DLEs in MOOCs for Language Learning. The main research question investigates whether and how the development of a comprehensive and interconnected narrative structure based on DST can enhance the participants’ learning experience in LMOOCs and facilitate language learning leading to better participant retention and higher completion rates. To illustrate and support the logic threads of the argumentation, the article introduces a mixed-methods or multi-modal study of three international LMOOCs in Norwegian for beginners (NfB) developed for the international e-learning platform FutureLearn (FL). The findings discussed in the article seem to corroborate the initial hypothesis that including a comprehensive narrative structure based on DST and inspired by principles of Connectivism can lead to the development of higher-quality DLEs in MOOCs, specifically in LMOOCs.

Since 1998, the New York City Police Department (NYPD) has been tasked with the collection and maintenance of crime data for incidents that occur in New York City public schools. The NYPD has provided this data to the New York City Department of Education (DOE). The DOE has compiled this data by schools and locations for the information of our parents and students, our teachers and staff, and the general public. In some instances, several Department of Education learning communities co-exist within a single building. In other instances, a single school has locations in several different buildings. In either of these instances, the data presented here is aggregated by building location rather than by school, since safety is always a building-wide issue. We use “consolidated locations” throughout the presentation of the data to indicate the numbers of incidents in buildings that include more than one learning community.

The pandemic in 2020 made online learning the widely used modality of teaching in several countries and it has also entered the spotlight of educational research. However, online learning has always been a challenge for disciplines (engineering, biology, and art) that require hands-on practice. For art teaching or training, online learning has many advantages and disadvantages. How art teachers embrace and adapt their teaching for online delivery remains an unanswered question. This research examines 892 art teachers' attitudes toward online learning, using learning environment, need satisfaction, mental engagement, and behavior as predictors. Structural equation modeling was used to explore the relationship between these four dimensions during these teachers' participation in an online learning program. The results reveal significant correlations between the learning environment, need satisfaction, mental engagement, and behavior. Moreover, this study reveals the group characteristics of art teachers, which can actually be supported by online learning programs. These findings provide insights into how art teachers view and use online learning, and thus can shed lights on their professional development.

Context

In an effort to understand some relationships between online teacher's class rates and other features, I scraped some data from an exposed API from a big online language teaching school.

Content

Contains data on: -Is_Tutor: is this teacher a tutor or not? -Up to 8 Teaching Languages and their respective levels -Up to 9 Spoken Languages and their respective levels -When did they sign up? -How many lessons have they taught? -Trial course price and course minimum price -Exam score and level (I think just for English?

Acknowledgements

API Connector: https://mixedanalytics.com/api-connector/

Banner image: https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.3plearning.com%2Fblog%2F7-essential-online-teaching-strategies-teachers-new-distance-learning%2F&psig=AOvVaw19DMSVEZj9cWaTdefuONig&ust=1592562803804000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCKCH6faUi-oCFQAAAAAdAAAAABAD

Inspiration

What relationship is there between teacher's salaries and how many languages they speak?

A teacher who is capable of carrying out his teaching duties if the stages of preparation, learning process and evaluation are carried out according to his design. The process that is often neglected is that the learning model used is sometimes unable to provide solutions to teachers. Curriculum changes that have occurred in Indonesia indicate that all education actors, including teachers, must be ready and improve themselves to follow the development of change. In line with the demands for changes in the 2013 Curriculum calls for changes in development from social in nature to democratic participation, for the sake of human resource growth. If so, education should be directed as a process: learning to know, learning to do, learning to live together, learning to be yourself (learning to be) and even lifelong learning (life long). learning), must adorn the lifestyle of a teacher, remembering that the teacher is an important figure in the process of change. This study intends to apply a learning model including: development of a syllabus and a Learning Implementation Plan (RPP) cooperative type time token type in PAK and Character In Class V, the material of Allah loves the world. The action hypothesis is a temporary answer in the form of action on the formulation of the problems set out in this classroom action research which is: student learning outcomes will increase "can be accepted. Based on the results of the implementation of classroom action research with the title implementation of the Jerrold E camp learning model in Christian education (PAK) and Character subjects in junior high schools, especially in Hauru Christian Middle School in class VIII which lasted for 2 research cycles, it can be concluded: Christian Educarion (PAK) and Character work effectively, so student learning outcomes will increase

Dive into the future of education with the Deep Learning Tutor Dataset – a pioneering resource designed to empower the creation of sophisticated, adaptive AI tutors. This dataset is meticulously curated to facilitate the fine-tuning of advanced large language models like GPT-4o, enabling them to internalize specialized pedagogical conversation patterns and expert teaching methodologies.

This collection represents a significant step towards developing intelligent educational systems that can truly adapt to individual student needs, provide nuanced feedback, and foster deeper understanding. By leveraging the power of deep learning and state-of-the-art LLMs, this dataset paves the way for a new generation of personalized learning experiences.

Key Features & Contents:

• Specialized Pedagogical Conversation Data: An extensive collection of educational dialogue, carefully structured to represent effective tutoring interactions. This includes examples of:
  • Expert Explanations: Clear, concise, and multi-faceted explanations of complex concepts.
  • Adaptive Feedback: Responses tailored to student understanding levels, common misconceptions, and learning styles.
  • Guided Inquiry: Dialogue patterns that encourage critical thinking and problem-solving.
  • Conceptual Clarification: Interactions focused on identifying and addressing misunderstandings.
  • Motivational Prompts: Examples of how to engage and encourage learners.
• Structured for Fine-tuning GPT-4o: The dataset is provided in a format optimized for fine-tuning OpenAI's GPT-4o, allowing the model to go beyond general knowledge and adopt a truly pedagogical persona.
• Foundational for Adaptive Tutoring Systems: This data is the bedrock for training AI systems that can dynamically adjust their teaching approach based on student performance, engagement, and learning progress.

Applications:

• Building Next-Generation AI Tutors: Develop intelligent tutors capable of empathetic, effective, and adaptive teaching.
• Research in AI in Education (AIEd): A valuable resource for researchers exploring the application of LLMs in educational contexts, dialogue systems, and personalized learning.
• Enhancing E-Learning Platforms: Integrate AI-driven tutoring capabilities into existing or new online learning environments.
• Developing Conversational AI for Learning: Train models to understand and generate educational dialogues that mimic expert human tutors.
• Personalized Learning Initiatives: Contribute to systems that offer highly individualized learning paths and support.

How to Leverage This Dataset: Fine-tuning Your AI Tutor

The primary utility of this dataset is to fine-tune a powerful LLM like GPT-4o, imbuing it with the specific conversational and pedagogical skills required for adaptive tutoring.

Prerequisites: * An OpenAI account with API access. * Familiarity with the OpenAI Platform and fine-tuning concepts.

Step 1: Download the Dataset Download the educational_conversation_data.jsonl file from this Kaggle dataset.

Step 2: Initiate GPT-4o Fine-tuning This process will train GPT-4o to emulate the expert teaching methodologies embedded within the dataset. 1. Upload Data: Navigate to the "Fine-tuning" section in your OpenAI Platform. Upload the educational_conversation_data.jsonl file. 2. Create Fine-tuning Job: * Base Model: gpt-4o (or gpt-4o-mini for more cost-effective experimentation). * Epochs: 3 (A common starting point; adjust based on dataset size and desired performance). * Learning Rate Multiplier: 2 (A good initial value; can be tuned). * Batch Size: 1 (Often effective for pedagogical data, but can be adjusted). * Note: These parameters are recommendations. Experimentation may be required to achieve optimal results for your specific application. 3. Start Job: Initiate the fine-tuning process. Once complete, you will receive a new custom model ID, representing your fine-tuned pedagogical AI.

Step 3: Integrate Your Fine-tuned Model The fine-tuned model ID can now be used with OpenAI's API to power your adaptive AI tutor. You can integrate it into: * A custom chat interface. * An existing educational platform. * A research prototype for conversational AI in education.

Files in This Dataset:

• educational_conversation_data.jsonl: The core dataset containing the specialized pedagogical conversation patterns and expert teaching methodologies, formatted for OpenAI fine-tuning.
• README.md: (Optional, but good practice) A brief overview of the dataset and usage.

This archive includes activities and resources originally published on the Homing Place website in 2004 to use alongside the interactive media projects 'way from home' and 'Take me to a place' that were also available on the site (see references below for current links to these projects). These projects were developed with refugee and asylum seeker organisations and inhabitants of Plymouth, UK. The resources were designed to complement and enhance teaching across a range of curriculum areas and stages. They include exercises drawn from the kinds of processes and methods of cultural and visual anthropology, geography, spatial art practices, writing and musical composition used to generate material in the projects along with ideas for discussion and links to other relevant resources.

The projects and exercises included are relevant to specific knowledge, skills and understanding outlined and required in the National Curriculum for England for a number of curriculum studies including Citizenship, Geography, Art and Design and Music and are pertinent to key government initiatives for e-learning, creativity and innovation in teaching and use of ICT and internet resources. The National Curriculum of England Correspondences section below will help you easily find those key areas of correspondence in more detail.