Here are 7 ways of big data and advanced analytics to improve teaching practices: 1. Data Sources in Education 2. The Role of Big Data in Education 3. Advanced Analytics in Education 4. Assessing Teaching Practices with Data 5. Enhancing Teaching Practices with Data
Unlocking Educational Potential: A Comprehensive Guide to Learning AnalyticsFuture Education Magazine
Learning Analytics refers to the measurement, collection, analysis, and reporting of data about learners and their contexts for understanding and optimizing learning and the environments in which it occurs.
Distance Learning, Online Teaching [19+ Years]
• Possess substantial strengths in distance learning, adult education, teaching with technology, student and faculty relations, higher education, and curriculum development.
• Significant experience as an adjunct online faculty member, Core Faculty, Dissertation Chair, Committee Member, Curriculum Developer/Author, and Faculty Development Manager.
• Create a safe, respectful, and welcoming learning environment.
• Specialize in working with new students, first generation students, and academically under-prepared students.
• Developed an exceptional record of academic excellence, end-of-course evaluations, collaboration, communication, mentoring, coaching, and professionalism.
• Computer proficient with online classroom platforms that include WebCT, eCollege, Canvas, Sakai, Moodle, Educator, Desire2Learn, Blackboard, Brightspace and others.
Dissertation Chair and Mentor [Remote, 11+ years]
• Provide high quality instruction, direction and mentorship for assigned students throughout all phases of the dissertation process.
• Provide timely and supportive mentoring throughout the student’s process of developing, researching, writing, and revising the dissertation.
• Participate in the Defense process of a student’s Prospectus and final Dissertation.
• Facilitate the successful completion of all IRB protocols.
Faculty Development [Remote, 10+ years]
• Served as a Trainer and Mentor for New Faculty Members.
• Performed faculty peer reviews and assessed classes based upon best practices and adult learning theories.
• Inspired faculty to improve their facilitation practice by leading online faculty workshops.
Curriculum Development [Remote, 12+ years]
• Authored hundreds of courses as a SME for multiple schools, including undergraduate and graduate courses.
• Strong knowledge and application of adult cognitive learning theories and instructional design methodologies.
• Develop content and assessments that met learning objectives, including discussions and assignments.
Background Includes: Various Online Schools (08/05 – Present)
Online Instructor, Doctoral Committee Member, Dissertation Chair, Faculty Development, Curriculum Development.
Unlocking Educational Potential: A Comprehensive Guide to Learning AnalyticsFuture Education Magazine
Learning Analytics refers to the measurement, collection, analysis, and reporting of data about learners and their contexts for understanding and optimizing learning and the environments in which it occurs.
Distance Learning, Online Teaching [19+ Years]
• Possess substantial strengths in distance learning, adult education, teaching with technology, student and faculty relations, higher education, and curriculum development.
• Significant experience as an adjunct online faculty member, Core Faculty, Dissertation Chair, Committee Member, Curriculum Developer/Author, and Faculty Development Manager.
• Create a safe, respectful, and welcoming learning environment.
• Specialize in working with new students, first generation students, and academically under-prepared students.
• Developed an exceptional record of academic excellence, end-of-course evaluations, collaboration, communication, mentoring, coaching, and professionalism.
• Computer proficient with online classroom platforms that include WebCT, eCollege, Canvas, Sakai, Moodle, Educator, Desire2Learn, Blackboard, Brightspace and others.
Dissertation Chair and Mentor [Remote, 11+ years]
• Provide high quality instruction, direction and mentorship for assigned students throughout all phases of the dissertation process.
• Provide timely and supportive mentoring throughout the student’s process of developing, researching, writing, and revising the dissertation.
• Participate in the Defense process of a student’s Prospectus and final Dissertation.
• Facilitate the successful completion of all IRB protocols.
Faculty Development [Remote, 10+ years]
• Served as a Trainer and Mentor for New Faculty Members.
• Performed faculty peer reviews and assessed classes based upon best practices and adult learning theories.
• Inspired faculty to improve their facilitation practice by leading online faculty workshops.
Curriculum Development [Remote, 12+ years]
• Authored hundreds of courses as a SME for multiple schools, including undergraduate and graduate courses.
• Strong knowledge and application of adult cognitive learning theories and instructional design methodologies.
• Develop content and assessments that met learning objectives, including discussions and assignments.
Background Includes: Various Online Schools (08/05 – Present)
Online Instructor, Doctoral Committee Member, Dissertation Chair, Faculty Development, Curriculum Development.
The Role of Data Science in the Future of E-Learning Analytics.pdfkherbalspiceltd
Dive into the future of work with comprehensive insights on professional development, education, data science, digital marketing, finance, artificial intelligence, and entrepreneurship. Transform your potential into expertise today – where learning meets innovation.
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement.
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement
Data Driven Instructional Decision MakingA framework.docxwhittemorelucilla
Data Driven
Instructional Decision Making
A framework
Data –Driven Instruction
Data-driven instruction is characterized by cycles
that provide a feedback loop
in which teachers plan and deliver instruction, assess student
understanding through the collection of data, analyze the data, and
then pivot instruction based on insights from their analysis.
From: Teachers know best: Making Data Work For Teachers and Students
Bill & Melinda Gates Foundation
https://s3.amazonaws.com/edtech-production/reports/Gates-TeachersKnowBest-MakingDataWork.pdf
Data-Driven Decision Making Process Cycle
Data Planning
and
Production
Data Analysis
Developing
an Action
Plan
Monitoring
progress
Measuring
Success
Implementing
the Action
Plan
Data is used
From : Teachers know best: Making Data Work For Teachers and Students
Bill & Melinda Gates Foundation
https://s3.amazonaws.com/edtech-production/reports/Gates-
TeachersKnowBest-MakingDataWork.pdf
Data –Driven Instruction Feedback Loop
Data Planning
and
Production
Data Analysis
Developing an
Action Plan
Monitoring
progress
Measuring
Success
Implementing
the Action
Plan
Data –Driven Instruction Feedback Loop
Data Planning
and
Production
Data Analysis
Developing an
Action Plan
Monitoring
progress
Measuring
Success
Implementing
the Action
Plan
Instructors need to
facilitate this data –driven
instruction decision loop
in a timely and smooth
fashion
…and on an ongoing basis
• Per student
• Per class
• Per group
Data –Driven Instruction Feedback Loop
Roles Inherent in the Data-Driven Instruction
Decision Making Loop
• Planner
• Data Producer
• Data Analyst
• Monitor
• Reporter
• Data End User
• IT
• Operations and Logistics
Data Planning and Production Questions
• What questions are to be addressed in future data-informed
conversations? Which questions are more important?
• What information (metrics) are needed to answer these question?
• Is the information available and feasibly attainable?
• Are the necessary technology and resources available?
• How can current non-data based instructional decision making be
mapped to data-based instructional decision making process?
• What are the costs associated with this endeavor?
• What are the timelines ?
• How and when will the data be collected and stored?
Data Analysis Questions
• What relations exists between the metrics? What patterns do
the data reveal?
• How many levels of the metric are needed to answer the
questions?
• Do the original questions need to be revised or expanded?
• Do the original metrics need to be redefined or expanded?
• What analytical tools are currently available? What tools
need to be designed to support the analysis?
• What method of analysis or evaluation will be used?
• What are the data limitations, strengths, challenges, context?
Monitor Questions
• How are the metrics evolving as the learning and instructional
processes evolve.
07 18-13 webinar - sharnell jackson - using data to personalize learningDreamBox Learning
Learning and competency data can be useful tools in assessing a student’s individual learning needs. In this month’s Blended Learning webinar, presenters Sharnell Jackson and Tim Hudson shared best practices for organizing and using student data in order to better meet student needs. They also discussed processes for using and analyzing data at the student, classroom, and district levels.
Recommendation of Data Mining Technique in Higher Education Prof. Priya Thaka...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Using Data to Drive Personalized Math Learning NeedsDreamBox Learning
Technologies to support data-driven decision-making hold great promise for increasing the effectiveness of teaching and learning activities, accelerating student achievement, and improving organizational performance. To access what students are learning and how they are progressing, educators can now use a continuous improvement framework for data-driven decision-making to organize people and processes to reach education objectives.
Join us for this webinar and discuss topics including:
• Building a sustainable data analysis framework
• Common challenges involved in establishing data-driven practices
• Incorporating blended learning environments to meet school goals
Here are 5 Benefits of PMHNP Certification: 1. Expertise and Competence 2. Enhanced Career Opportunities 3. Quality of Care 4. Patient Confidence and Trust 5. Interdisciplinary Collaboration
Unlocking Opportunities: The Impact and Significance of Sports ScholarshipsFuture Education Magazine
These sports scholarships, often offered by colleges and universities, are financial awards granted to students based on their athletic abilities. These scholarships serve as a means to recruit talented athletes to enhance the sports programs of educational institutions.
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Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement.
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement
Data Driven Instructional Decision MakingA framework.docxwhittemorelucilla
Data Driven
Instructional Decision Making
A framework
Data –Driven Instruction
Data-driven instruction is characterized by cycles
that provide a feedback loop
in which teachers plan and deliver instruction, assess student
understanding through the collection of data, analyze the data, and
then pivot instruction based on insights from their analysis.
From: Teachers know best: Making Data Work For Teachers and Students
Bill & Melinda Gates Foundation
https://s3.amazonaws.com/edtech-production/reports/Gates-TeachersKnowBest-MakingDataWork.pdf
Data-Driven Decision Making Process Cycle
Data Planning
and
Production
Data Analysis
Developing
an Action
Plan
Monitoring
progress
Measuring
Success
Implementing
the Action
Plan
Data is used
From : Teachers know best: Making Data Work For Teachers and Students
Bill & Melinda Gates Foundation
https://s3.amazonaws.com/edtech-production/reports/Gates-
TeachersKnowBest-MakingDataWork.pdf
Data –Driven Instruction Feedback Loop
Data Planning
and
Production
Data Analysis
Developing an
Action Plan
Monitoring
progress
Measuring
Success
Implementing
the Action
Plan
Data –Driven Instruction Feedback Loop
Data Planning
and
Production
Data Analysis
Developing an
Action Plan
Monitoring
progress
Measuring
Success
Implementing
the Action
Plan
Instructors need to
facilitate this data –driven
instruction decision loop
in a timely and smooth
fashion
…and on an ongoing basis
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• Per class
• Per group
Data –Driven Instruction Feedback Loop
Roles Inherent in the Data-Driven Instruction
Decision Making Loop
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• Data Producer
• Data Analyst
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• What questions are to be addressed in future data-informed
conversations? Which questions are more important?
• What information (metrics) are needed to answer these question?
• Is the information available and feasibly attainable?
• Are the necessary technology and resources available?
• How can current non-data based instructional decision making be
mapped to data-based instructional decision making process?
• What are the costs associated with this endeavor?
• What are the timelines ?
• How and when will the data be collected and stored?
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the data reveal?
• How many levels of the metric are needed to answer the
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• Do the original questions need to be revised or expanded?
• Do the original metrics need to be redefined or expanded?
• What analytical tools are currently available? What tools
need to be designed to support the analysis?
• What method of analysis or evaluation will be used?
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• How are the metrics evolving as the learning and instructional
processes evolve.
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Big Data and Advanced Analytics For Improving Teaching Practices In 2023 | Future Education Magazine
1. How Big Data and Advanced
Analytics Can Be Leveraged to
Assess and Enhance Teaching
Practices?
S
H
A
R
E
Education has always been a dynamic field, constantly evolving to meet the changing needs of students
and society. In recent years, the integration of technology into classrooms has generated an abundance of
data that holds immense potential for improving teaching practices and student learning. Big data,
characterized by its volume, velocity, and variety, combined with advanced analytics techniques, has
ushered in a new era of educational research and practice. This article will explore how big data and
advanced analytics can be leveraged to assess and enhance teaching practices
Table of Contents
Here are 7 ways of big data and advanced analytics to improve teaching practices:
o Data Sources in Education
o The Role of Big Data in Education
o Advanced Analytics in Education
2. o Assessing Teaching Practices with Data
o Enhancing Teaching Practices with Data
o Challenges and Ethical Considerations
o Future Directions in Educational Data Analysis
o Conclusion
Here are 7 ways of big data and advanced analytics to improve
teaching practices:
Before delving into the role of big data and advanced analytics in education, it’s essential to understand the
diverse sources of data generated within educational settings.
Data Sources in Education
1. Administrative Data
This includes student enrollment records, attendance data, and demographic information. Administrative
data are typically stored in school or district databases and are used for tracking student progress and
allocating resources.
2. Assessment Data
These data encompass the results of standardized tests, quizzes, and assignments. They provide insights
into student performance, strengths, and areas requiring improvement.
3. Learning Management Systems (LMS)
LMS platforms, such as Moodle or Canvas, generate data on student engagement, participation, and
progress within online courses. They track learners’ interactions with course materials and activities.
3. 4. Educational Technology
Data from educational technology tools and platforms, including online textbooks, e-learning apps, and
interactive simulations, can offer valuable insights into student behavior and learning patterns.
5. Surveys and Questionnaires
Educational institutions often administer surveys and questionnaires to gather feedback from students,
parents, and educators. These surveys provide qualitative and quantitative data on various aspects of
education.
6. Social Media and Online Forums
Educational discussions on social media and online forums generate user-generated content and
discussions that can be analyzed to understand trends, challenges, and emerging topics in education.
7. Sensor Data
In some cases, sensors and wearable devices can collect data on student movements, interactions, and
physiological responses, providing insights into their physical and emotional well-being.
The Role of Big Data in Education
Big data in education refers to the vast and complex datasets generated by various educational systems and
technologies. These datasets offer a wealth of information that can be harnessed to gain a deeper
understanding of educational processes and outcomes. The role of big data in education is multifaceted:
1. Predictive Analytics
4. Big data allows educators to predict student performance and behavior patterns. By analyzing historical
data, such as grades, attendance, and engagement, predictive analytics can identify at-risk students who
may need additional support.
2. Personalization
Big data enables personalized learning experiences. By tracking individual progress and preferences,
educators can tailor instruction and resources to meet the specific needs of each student.
3. Curriculum Development
Educational institutions can use data to develop and refine curriculum materials, ensuring that they align
with learning objectives and student needs.
4. Resource Allocation
Schools and districts can optimize resource allocation based on data analysis, directing funding and
support to areas where they are most needed.
5. Performance Assessment
Big data allows for comprehensive performance assessment of educational programs, policies, and
interventions. Schools can measure the impact of teaching practices and make data-driven decisions for
improvement.
6. Research and Innovation
5. Researchers in education can leverage big data to conduct studies and experiments on a scale that was
previously unimaginable. This enables the exploration of new pedagogical methods and the identification
of best practices.
Advanced Analytics in Education
Advanced analytics techniques are essential for extracting meaningful insights from educational data.
These techniques encompass a range of data analysis methods, including:
1. Descriptive Analytics
Descriptive analytics involves summarizing and visualizing data to provide an overview of educational
trends and patterns. It includes techniques such as data visualization and summary statistics.
2. Predictive Analytics
Predictive analytics uses historical data to build models that can predict future events or trends. In
education, this may involve predicting student performance, dropout rates, or course completion.
3. Prescriptive Analytics
Prescriptive analytics goes beyond prediction by recommending actions to achieve specific outcomes. In
education, it can help identify interventions and strategies to support struggling students.
4. Machine Learning
Machine learning algorithms can identify complex patterns and relationships in educational data. This
includes techniques like classification, clustering, and regression analysis.
6. 5. Natural Language Processing (NLP)
NLP techniques analyze text data, such as student essays or forum discussions, to extract insights about
language use, sentiment, and topic trends.
6. Social Network Analysis (SNA)
SNA examines the connections and interactions between students, teachers, and educational content. It can
reveal information about social dynamics and collaborative learning patterns.
Assessing Teaching Practices with Data
Assessing teaching practices is a fundamental application of educational data analysis. It performs an
important role in big data and advanced analytics. By analyzing data from various sources, educators and
researchers can gain insights into the effectiveness of teaching methods, instructional materials, and
classroom strategies. Here are some ways in which data can be used to assess teaching practices:
1. Evaluating Student Outcomes
Educational data can be used to assess student outcomes, such as test scores, grades, and course
completion rates. By comparing outcomes across different teaching methods or instructors, educators can
identify effective approaches.
2. Observational Data
Classroom observations, either in person or through video recordings, can provide valuable data on
teacher-student interactions, instructional strategies, and classroom management. These observations can
be used to assess teaching practices and provide feedback for improvement.
3. LMS and Online Activity Data
Learning Management Systems and online learning platforms generate data on student engagement and
interactions with course materials. Analyzing this data can help assess the effectiveness of online teaching
practices.
4. Feedback Surveys
Surveys administered to students can collect feedback on teaching practices, course content, and the
learning experience. Analyzing survey data can highlight areas where improvements are needed.
5. Assessment Analytics
Assessment data, such as item analysis and student responses to quizzes and exams, can reveal the
strengths and weaknesses of instructional materials and teaching methods.
6. Comparative Analysis
Comparative analysis involves comparing the performance of students in different classrooms or under
different instructors. This can help identify teaching practices that lead to better learning outcomes.
7. Enhancing Teaching Practices with Data
The true value of educational data analysis lies in its ability to inform and enhance teaching practices. Here
are some ways in which data can be used to improve instruction:
1. Identifying At-Risk Students
Predictive analytics can help identify students who are at risk of academic failure. Teachers can then
provide targeted support and interventions to help these students succeed.
2. Personalized Learning
By analyzing individual student data, teachers can tailor instruction to meet each student’s specific needs.
Adaptive learning platforms use data to adjust content and pacing in real time.
3. Feedback Loops
Continuous feedback loops, facilitated by data, allow teachers to adjust their teaching methods based on
student responses and outcomes. This iterative process promotes ongoing improvement.
4. Curriculum Enhancement
Educational data can inform curriculum development and enhancement. By identifying areas where
students struggle, educators can refine instructional materials and resources.
5. Professional Development
Data-driven insights can guide professional development for teachers. Educators can identify areas where
they need additional training or support to improve their teaching practices.
8. 20 Best Platforms to Learn Data Science and Machine Learning
Data science is exploding in practically every region of the planet. Data scientists are in great demand
because they seem to have the “magical” capacity to generate value from data for data-driven businesses
and organizations.
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6. Innovation and Experimentation
Researchers and educators can use data to experiment with new teaching methods and innovations. These
experiments can lead to the discovery of effective pedagogical approaches.
7. Policy and Decision Making
Educational leaders can use data to inform policy decisions and resource allocation. Data can highlight
areas where investment is needed and where policies may need adjustment.
Challenges and Ethical Considerations
While the potential benefits of using big data and advanced analytics in education are significant, several
challenges and ethical considerations must be addressed:
1. Data Privacy and Security
Safeguarding student data is paramount. Schools and institutions must implement robust data privacy and
security measures to protect sensitive information.
9. 2. Bias and Fairness
Data analysis can introduce bias, particularly when historical data reflects societal biases. Care must be
taken to ensure fairness and equity in decision-making.
3. Interpreting Complex Data
Educational data can be complex and multifaceted. Interpretation requires expertise and careful
consideration of context.
4. Teacher Autonomy
Teachers may be concerned about perceived loss of autonomy and increased surveillance when data is
used for assessment and evaluation.
5. Resource Constraints
Some educational institutions may lack the resources, including staff with data analysis expertise, to
effectively utilize big data and advanced analytics.
6. Ethical Data Use
The ethical use of data includes obtaining informed consent, protecting student anonymity, and ensuring
that data is used for educational purposes rather than commercial gain.
Future Directions in Educational Data Analysis
The field of educational data analysis is continuously evolving. Here are some future directions and
emerging trends:
1. Ethical Data Use Frameworks
The development of ethical frameworks and guidelines for using educational data will become increasingly
important. This will help ensure responsible data practices.
2. AI and Machine Learning in Education
The integration of artificial intelligence and machine learning into education will enable more personalized
and adaptive learning experiences. It performs an important role in big data and advanced analytics.
3. Blockchain for Credentialing
Blockchain technology may be used to securely store and verify academic credentials, making the
credentialing process more transparent and reliable.
4. Continuous Assessment
Continuous assessment and feedback mechanisms will become more prevalent, allowing for real-time
adjustments to teaching practices.
10. 5. Collaborative Data Initiatives
Collaborative data initiatives will allow educational institutions to pool resources and expertise for more
comprehensive data analysis.
6. Learner Analytics
Learner analytics will focus on providing students with insights into their own learning patterns and
strategies, empowering them to take greater control of their education.
7. Data-Driven Decision Making
Educational leaders will increasingly rely on data-driven decision-making processes to improve
educational outcomes and resource allocation.
Conclusion
The era of big data and advanced analytics has ushered in a new age of possibilities in education. By
harnessing the power of data, educators and researchers can gain insights into teaching practices that were
previously elusive. This data-driven approach has the potential to revolutionize education, making it more
personalized, effective, and equitable.
However, as we navigate this data-rich landscape, it is essential to remain mindful of ethical considerations
and privacy concerns. Ensuring that data is used responsibly and in the best interests of students is
paramount.
In conclusion, the integration of big data and advanced analytics into education represents a transformative
force that has the potential to enhance teaching practices and ultimately improve the educational
experiences and outcomes of students. It is an exciting journey into the future of education, where data-
driven insights illuminate the path to excellence in teaching and learning.