Background:
Online education has been around for a while, but it recently gained popularity due to factors other than technology. Peter Norvig, a former professor and co-author of a textbook, experienced the limitations of traditional textbook publishing.

Feedback Loop:
Norvig realized that textbooks lack feedback from students, who are the best judges of what is important and confusing. He emphasizes the need to close the feedback loop between students and educators to improve educational materials.

Interactive Learning:
Norvig highlights the loss of interactive learning experiences for students using traditional textbooks. Students do not get to manipulate data, rotate 3D surfaces, or change variables to see how they affect the results.

Hal Abelson’s Advice:
Norvig sought advice from Hal Abelson, an MIT professor and pioneer in online education. Abelson recommended a paper by Benjamin Bloom on the two-sigma effect.

Two-Sigma Effect:
Bloom’s studies showed that one-on-one tutoring with mastery learning can improve student performance by two standard deviations. This means that 98% of students can achieve above-average results with this approach. Not all studies have replicated this exact effect, but the two-sigma effect is significant because most educational interventions show no measurable impact.

Motivation for Online Education:
The need for affordable and accessible education The cost of traditional tutoring is high, and society is unwilling to pay for it.

Duplicating the Success of One-on-One Tutoring:
Stanford professors Peter Norvig and Sebastian Thrun aim to replicate the success of one-on-one tutoring through online education.

Initial Attempts:
Initial efforts involved recording lectures and making them available online. However, these attempts lacked the personalized interaction of one-on-one tutoring.

Creating a More Personalized Experience:
Norvig and Thrun decided to create a more personalized experience by recording lectures in a casual, conversational style. The goal was to simulate the feeling of one-on-one tutoring, even though the lectures were recorded in advance.

Unanticipated Success:
The online course attracted an overwhelming number of students, with 160,000 students from 209 countries signing up. This success prompted other universities and organizations to launch their online courses.

Stanford’s Contribution to Online Education:
Stanford professors were among the first to offer online courses, pushing the boundaries of traditional education. Coursera, Udacity, and edX were all founded by Stanford professors and researchers.

Google’s Involvement in Online Education:
Google launched its online course, Power Searching with Google, which attracted over 150,000 students. Google open-sourced the technology used to build Power Searching, enabling others to create their own online courses.

Rapid Growth and Global Interest:
In just a few months, several universities and nonprofits have created and offered online courses using Google’s open-source platform, Course Builder. The global interest in online education continues to grow rapidly.

Question for Discussion:

Why has online education gained such popularity in recent years?

Important Concepts and Insights:
The rise of online learning platforms was not due to technological advancements alone but rather the convergence of several factors, including a large pool of interested individuals and frustration with the cost of traditional education. Motivation is crucial for effective learning. Access to information alone is not enough; students need to be motivated to engage with the material and put in the effort to learn. The educational moment lies in the decision to seek knowledge, not just in finding the answer. It is more important to ask questions than to simply receive answers. Due dates and peer interaction play important roles in maintaining motivation. Due dates create a sense of urgency, while peers provide support and feedback. Online learning platforms should focus on optimizing motivation rather than information. This can be done by incorporating elements such as due dates, peer interaction, pride in accomplishment, and authenticity. Constructivism is an educational approach that emphasizes the active construction of knowledge by the learner. It involves hands-on experiences, collaboration, and critical thinking. Online learning platforms can facilitate constructivist learning by providing interactive materials, discussion forums, and opportunities for feedback and assessment.

Valuable Lessons:
Online learning requires more than just technology; it necessitates a focus on motivation and engagement. Due dates and peer interaction are key motivators in online learning. Online platforms can harness the power of pride and authenticity to enhance motivation. Constructivism is an effective approach for online learning, promoting active learning and knowledge construction.

Learning is Student-Centered:
Learning is an active process done by students, not something teachers do. Teachers should focus on facilitating learning rather than just delivering content.

Confronting Misconceptions:
Giving students the right model may not displace their misconceptions. To correct misconceptions, students need to confront and resolve them through problem-solving.

Encouraging Wrong Answers:
Assigning problems where students don’t know the solution can be beneficial. Students should be encouraged to come up with wrong answers to identify and address misconceptions.

The Role of UI in Learning:
User interfaces (UIs) can be designed to encourage exploration and experimentation. Well-designed UIs can make it easier for students to learn from their mistakes.

Open-Ended Activities:
Multiple-choice and fill-in-the-blank questions limit learning. Open-ended activities, especially in programming, allow for more creativity and problem-solving.

Automatic Grading:
Automatic grading enables open-ended activities and provides immediate feedback. Peer grading and self-grading can also be effective in certain contexts.

Test-Driven Development in Education:
Similar to test-driven development in software engineering, textbooks should be written around the problems students need to solve. This approach ensures that students can apply their knowledge to real-world scenarios.

Test-Driven Textbook Writing:
Write textbook chapters only when tests fail, ensuring that each section directly addresses a specific learning objective.

Individualization:
Current online courses lack individualization beyond right/wrong feedback. Mixed initiative learning allows for shorter video segments and more frequent interactions to keep students engaged.

Multi-Path Learning:
Adapt learning paths to accommodate students’ backgrounds, misconceptions, and interests.

Socialization:
Online classes should prioritize student-student interactions over teacher-student interactions. Discussion forums can foster socialization, but they need to cater to students with diverse backgrounds and abilities.

Flexibility:
Online courses should offer flexible scheduling options to accommodate students’ different needs and life circumstances.

AI Class as a Train Schedule:
Run online courses like a train schedule with multiple trains leaving the station at different times, allowing students to join or leave as needed. Offer express and slow trains to accommodate different learning paces.

Challenges of Student Load:
Unexpectedly high student enrollment stressed the online platform, leading to potential technical issues. Cutting back on certain features and delaying forum availability proved beneficial, encouraging students to engage in discussions on various social media platforms. Analytics suffered due to the lack of data from centralized discussions, hindering the ability to correlate discussion participation with exam performance.

Intimacy and Identity:
Students formed smaller, more intimate groups on external platforms, fostering a stronger sense of identity and ownership. This shift away from a single forum to multiple platforms resulted in a sense of pride and ownership among students who actively participated in creating and managing their discussion groups.

Continual Improvement and Flexible Learning:
The involvement of experts in machine learning highlights the potential for data analysis and improvement in online education. Moving away from a rigid class structure towards a flexible environment with multiple paths and authors can enhance personalization and cater to diverse learning styles. This model allows for a more dynamic and adaptive learning experience, where students can choose the most appropriate content and sequence for their individual needs.

Evaluation and Portfolios:
The traditional transcript provides limited information about a student’s performance and abilities. A portfolio approach, showcasing a student’s work and achievements, can provide a more comprehensive representation of their learning. This shift towards portfolios can better capture a student’s skills, competencies, and progress over time.

GPA vs. Portfolio:
Traditional hiring processes often prioritize GPA as an indicator of academic achievement. For certain fields like arts or skills-based roles, evaluating a portfolio of work may provide a more accurate assessment of an individual’s abilities. Portfolios can showcase practical skills and creativity, giving a clearer picture of a candidate’s potential.

Education as Big Data:
Peter Norvig compares education to big data analysis. With limited feedback (e.g., grades in a few classes), learning can be inefficient. The hope is that by increasing the frequency of feedback (e.g., through real-time monitoring of interactions), education can improve significantly.

Challenges in Practice:
While the concept of using data to improve education is promising, practical implementation may face challenges.

Edison’s prediction of a motion picture revolution in education:
In 1913, Thomas Edison believed motion pictures would replace books in schools, revolutionizing education within a decade. This prediction has been echoed by proponents of various technologies over the years, but none have fully succeeded.

Broadcast media and the lack of interaction:
Technologies like motion pictures, radio, television, and video cassettes aimed to deliver content to students, but they lacked interactive elements.

Personal computers and the limited feedback loop:
Personal computers introduced some interaction, but the feedback loop was still limited.

The potential of modern technology for effective feedback and improvement:
Current technologies offer the opportunity to gather extensive data on student learning, enabling rapid improvements in educational practices.

One-on-one teaching and cost-effective peer-to-peer interaction:
One-on-one teaching is ideal, but it’s costly. Peer-to-peer interaction, where students learn from each other, can be a more feasible and effective approach.

Assessment and teaching to the right goals:
Optimizing education towards specific assessment tools can be problematic if the assessment is too narrow. It’s important to define the desired outcomes and teach towards those goals, rather than focusing solely on test performance.

The challenge of short feedback loops and forgetting:
Students may learn specific skills during instruction but forget them over time. Revisiting and reinforcing learned concepts at appropriate intervals is essential.

Inobtrusive testing and combining simple and complex problems:
Testing should be designed to assess specific skills without being overly burdensome for students. Combining simple and complex problems allows for efficient assessment of multiple skills.

Beyond students teaching students:
Student-to-student teaching is one aspect of personalized learning. Interaction with simulations and environments and moving away from lectures are also important components.

Prospects for true automated tutoring:
Fully automated tutoring systems comparable to human tutors may be possible in narrow domains with simple interaction channels.

Expert Systems in Education:
Expert systems, such as those developed at Carnegie in the 80s, have been used for teaching in limited domains with mixed results. Independent evaluations of Carnegie learning systems showed varying levels of effectiveness. The success of expert systems in education depends on the availability of human experts to provide comprehensive knowledge engineering.

Challenges of Knowledge Engineering:
The amount of work required for knowledge engineering makes it impractical to apply expert systems to every subject. The process of identifying and cataloging all potential bugs in a subject is time-consuming and requires significant effort.

Potential for Automated Bug Identification:
Instead of relying solely on human experts, researchers are exploring the possibility of using learning systems to automatically identify patterns of errors. By identifying patterns in student errors, learning systems can provide targeted interventions to help students overcome specific challenges. Human analysts can then review these patterns and provide additional insights to improve the system’s interventions.

Need for Data and Analysis:
The feasibility of using automated bug identification and intervention methods depends on the availability of sufficient data and the ability to analyze it effectively. Researchers are actively exploring whether enough data can be gathered and analyzed to make these methods viable.

Accuracy and Verification in Online Learning:
Verification of information presented in online courses is crucial to ensure validity. Incorrect information can lead to problems rather than solutions. Online learning platforms provide a marketplace for rapid correction of errors, unlike textbooks.

Breaking Down Units in Online Learning:
Breaking down learning units into smaller components allows for more flexible learning paths. Combining materials from different authors can create the best possible learning path. Consistency in style, terminology, and notation is essential to avoid confusion.

Comparison to Wikipedia:
The effort required to create a class is significantly higher than contributing to Wikipedia. Wikipedia allows for small contributions like editing sentences or adding facts. Creating a full online course requires more work and different incentives.

Incentives for Course Creation:
Finding the right incentives to encourage individuals to create online courses is challenging. Most popular online courses are at the college level, with fewer options for middle and high school levels. Khan Academy’s success may be a factor in this observation.

Challenges of Online Learning:
Financial feasibility: Universities may offer easier financial paths, more free time, and computer expertise, making them more attractive for online education startups. District-by-district adoption: In the US, targeting individual school districts can be daunting for startups, making university-level adoption more feasible. Verification of cheating: Online learning poses challenges in verifying the authenticity of student achievements and preventing cheating.

Credentials and Course Credit:
Current focus: Online learning platforms often provide unofficial certificates or PDFs with signatures, but their value is debatable. Potential routes: Possible solutions include accepting online learning achievements without formal accreditation, seeking accreditation through traditional universities, or establishing new online-focused institutions granting certificates.

Education Beyond Memorization:
Mechanics vs. Capabilities: Some argue that education should focus on teaching students what is doable rather than just the mechanics of doing it, allowing them to create and innovate. Continuing Education: Online learning is better suited for continuous learning and just-in-time education, allowing students to access resources and acquire knowledge as needed throughout their lives.

Data Collection and Improvement:
Data collection challenges: Despite collecting data on online learning, the results have been limited due to a lack of rich interactions and a standardized learning sequence for all students.

Data Collection Challenges:
The evaluation of educational programs is challenging due to the lack of experimental frameworks. Traditional methods of comparing student performance in different groups (e.g., A vs. B) have not been widely implemented. As a result, the available data is limited and may not provide a comprehensive assessment of program effectiveness.

Evaluation of a Searching Class:
An example of data collection and evaluation is a searching class run by Google. The effectiveness of the class is evaluated by tracking the participants’ search behavior and performance after taking the class. This evaluation approach allows Google to measure the impact of the class on the participants’ ability to search effectively.

Challenges in Data Sharing:
Despite the progress in opening up education to a wider audience, there are concerns about data sharing. Sharing fine-grained data can raise confidentiality issues, especially in K-12 education where regulations are stricter. Balancing the need for detailed analysis with the protection of student privacy is a challenge that needs to be addressed.

Aggregate Data Sharing:
Sharing aggregate data, such as mean scores on standardized tests, is generally acceptable and does not raise confidentiality concerns. However, sharing more fine-grained data requires careful consideration of the privacy implications and the development of appropriate data-sharing models. These models should ensure that both students and educational institutions consent to the sharing of data in a way that protects confidentiality.