Peter Norvig (Google Director of Research) – Google I/O 2014 – Making sense of online course data (Jun 2014)

Chapters

Abstract

Data Abundance in Online Education: Opportunities and Insights for Enhanced Learning

In the rapidly evolving landscape of online education, the abundance of data presents unprecedented opportunities for enhancing learning experiences. This comprehensive exploration delves into various facets of online education, highlighting the critical roles of data availability, MOOCs (Massive Open Online Courses), self-evaluation techniques, student engagement strategies, and the future trajectory of online learning.

The Power of Data in Online Education

Online education is a treasure trove of data, offering unique insights into student behaviors, learning patterns, and educational outcomes. This data richness facilitates continuous improvement and personalized learning experiences. Essential for managing the vast volume of data in online education are data visualization and analysis tools. Dashboards and real-time monitoring enable educators to track key metrics, spot trends, and make timely adjustments. Machine learning algorithms are transforming educational strategies by analyzing student behavior and predicting potential issues. Understanding common student errors leads to more effective feedback and improved learning tools through error analysis in compiler improvement. Item Response Theory in problem evaluation assesses the difficulty level of problems and student capabilities, highlighting discrepancies in performance. Additionally, data analysis can uncover instructional design flaws in case studies involving ambiguous problems, leading to necessary revisions.

Understanding MOOCs and Their Impact

MOOCs have redefined the landscape of online education with their massive scale, openness, online nature, and structured course format. Google’s involvement, such as launching courses like “Power Searching with Google” and releasing Course Builder’s open-source code, has significantly propelled the ecosystem forward. In these courses, self-evaluation was experimented with, leading to promising alignment between student and instructor assessments. Factors for effective self-evaluation include clear rubrics and meaningful projects. Understanding student motivations is crucial for addressing the low completion rates in MOOCs, as research reveals diverse goals among participants. The future of MOOCs may involve more personalized content based on student interests and backgrounds, enhancing engagement and achievement.

Challenges and Innovations in Online Learning

Online education faces various challenges, including privacy concerns in forum post analysis, balancing course modularity with educational needs, and managing student engagement. Addressing privacy is vital for leveraging forum discussions and protecting student information. Balancing structured schedules with flexibility can help maintain student engagement while accommodating diverse needs. While Google Analytics is useful for course analysis, custom dashboards are developed for more nuanced insights. Exploring the impact of technical factors on dropout rates offers valuable insights for improving course design and delivery. Balancing student expectations with pedagogical expertise remains a challenge in course design. Scheduled courses promote motivation and prevent dropouts, but some students prefer flexible, self-paced learning. A “bus model” approach with scheduled stops and the option to pause learning may be a solution. Instructors should control the content sequence, but students should have some choice in content selection.

Demographics and Future Directions

MOOC demographics currently skew towards well-educated, English-speaking individuals, primarily in the 35-45 age range. This raises questions about the broader applicability and inclusivity of online education. Future directions include collaborations like MOOC.org for greater platform interoperability and content sharing. The traditional four-year college model may transform due to the rising importance of lifelong learning and reskilling. The future balance between traditional colleges and online institutions remains uncertain, highlighting the dynamic nature of education.

Mining Valuable Insights from Educational Data to Enhance Online Learning Effectiveness

Data-Driven Approach to Online Education:

Online classes provide a wealth of data for understanding student performance and learning trends. It is essential to collect and analyze this data to improve the effectiveness of online education. Simple dashboards can provide valuable insights into student engagement and performance.

Monitoring Student Progress:

Tracking the number of users on the site over time identifies patterns of participation and drop-off rates. Dashboards visualize student progress and identify areas of struggle. Promptly addressing issues by fixing errors or providing additional support is crucial.

Leveraging Machine Learning for Deeper Analysis:

Using clustering algorithms helps identify common patterns in student submissions, aiding educators in pinpointing specific areas where students may need assistance. Analyzing student errors improves compiler error messages, making them more helpful for beginners. Developing specialized compilers based on past student mistakes provides tailored feedback and guidance.

Item Response Theory and Problem Difficulty:

Item response theory aids in understanding the relationship between problem difficulty and student capabilities. Plots of student performance can reveal anomalies, like problems where high-performing students score lower than expected. Investigating these anomalies uncovers issues such as ambiguities in problem wording or compensating errors.

Utilizing Data to Enhance Online Learning:

Data analysis enables educators to identify problems, address misconceptions, and provide personalized support to students. Data-driven insights can lead to continuous improvement of online courses, resulting in better learning outcomes. Online education has the potential to revolutionize education by providing tailored and effective learning experiences for students.

Julia Wilkowski’s Perspective on the Design and Outcomes of Google’s MOOCs

MOOC Definition:

MOOCs, as defined by Julia Wilkowski, are massive, open, online, and structured as courses. They can have up to 150,000 registrants, are free and available to anyone with an internet connection. These courses are accessible online and have a defined start and end date, instructor involvement, and a sense of community.

Google’s Role in Online Education:

Google aims to discover effective methods in online education to contribute to ecosystem growth, without the belief that MOOCs will replace traditional universities.

Google’s Initial MOOC:

“Power Searching with Google,” launched in July 2012 with 150,000 registrants and 40,000 completions, aimed to teach efficient and effective use of Google search tools. Following its initial launch, the course was re-released with bug fixes and the release of Course Builder’s open-source code. Since then, 94 partners have used the Course Builder code for their own MOOCs or flipped classroom approaches.

Subsequent Google MOOCs:

These include “Advanced Power Searching,” “Implementing Web Accessibility Best Practices for Web Developers,” “Mapping with Google,” and “Making Sense of Online Data.”

Online Course Evaluation and Student Self-Assessment in Massive Open Online Courses (MOOCs)

Research Areas:

Research in MOOCs involves scaling evaluation mechanisms and understanding student registration and motivation. In the “Advanced Power Searching Course,” students were taught to solve complex research challenges using Google tools. The course used traditional online methods like video lectures, activities, and multiple-choice tests, with an evaluation system where students submitted case studies of their search problems. Results showed significant alignment between student and TA scores, indicating the effectiveness of self-evaluation.

Improvements:

The criteria for self-evaluation were made more objective, and students received more detailed feedback on their practice calibration exercises. This helped improve their self-evaluation skills.

Rubrics and Value:

A clear rubric and criteria for evaluation improved student grading accuracy. Students found the mapping project more worthwhile than the search project, correlating with more accurate grading. Self-evaluation can be effective in low-stakes environments.

Testing Rubrics:

Testing rubrics in advance is important to identify subjective versus objective measures, and teaching students how to evaluate their own work can improve metacognitive skills.

MOOC Completion Rates:

Only about 20% of students complete MOOCs. Many people register for MOOCs with various goals, such as earning certificates, learning new skills, or exploring course content. In the Mapping with Google course, for example, 40,000 people registered, and 20,000 were active students.

Participants’ Course Completion Intentions:

Only slightly over half of the registrants intended to complete the course, challenging traditional educational assumptions that all students aim to complete a course linearly.

Measuring Goal Achievement:

Researchers analyzed course data to determine the extent to which participants achieved their stated goals. For those aiming to learn new skills or specific features, nearly two-thirds achieved their goals by engaging with relevant videos, activities, or text lessons. Even for participants seeking certificates, a significant proportion successfully met this more rigorous goal.

Implications for Course Design and MOOC Platforms:

Making course content available upfront, rather than following a traditional metered release schedule, can better accommodate diverse learner goals and preferences. Encouraging learners to explore and select relevant content aligns with the varied motivations for MOOC participation.

Design Elements in Evolution:

Providing personalized emails with links to specific lessons based on students’ interests and backgrounds, allowing students to create their own playlists by choosing from a list of course content, and offering predicted learning paths based on students’ backgrounds and goals are among the evolving design elements in online education.

Forum Analysis:

Limited sophisticated analysis of forum posts is due to deletion after 60 days for privacy compliance. Potential for sentiment analysis to gauge positive or negative feedback exists, but privacy concerns regarding linking forum identity to student identity and sharing information persist.

Online Course vs. Reference:

Udacity instructor highlights the distinction between online courses and online references. Online references offer a vast amount of information, while online courses provide structure and assessment. Udacity uses data to improve the quality and personalization of its courses.

Measuring Learning:

Stanford focuses on assessing students’ problem-solving skills and higher-order thinking. Multiple-choice questions measure basic knowledge, while free-response questions assess complex reasoning. The use of peer assessment promotes critical thinking and feedback.

Challenges in MOOC Assessments:

Assessing higher-order thinking skills through automated grading is challenging. The need for human graders for free-response questions can be costly and time-consuming. Potential biases in automated grading systems need to be addressed.

Impact of MOOCs on Education:

MOOCs provide high-quality education to students who may not have access to traditional educational institutions. However, the challenge lies in developing assessment methods that accurately measure learning outcomes and ensure academic integrity.

Personalized Learning and Designing Online Courses

Activities First, Videos Second:

Despite the preference for videos, data shows that activities promote better learning. Instructors are encouraged to prioritize activities before videos.

Scaffolding for Learning Paths:

Learners need guidance when creating their own learning paths. Providing hints, maps, or options can help learners choose more effectively. While highly motivated learners may thrive without guidance, most need structure.

Benefits for Training Designers and Instructors:

Instructors can tailor courses to individual learner goals, increasing motivation and completion rates. Flipped learning, which focuses on skills rather than lectures, is beneficial in this regard.

Flipped Learning Process:

The process involves defining the skill students should acquire, translating the skill into an online exercise, designing the exercise before creating instructional content, and minimizing lecturing, focusing instead on introducing and explaining the problem.

Different Levels of Students:

MOOCs attract a diverse range of learners. Younger learners prefer visual content and immediate feedback, while older learners prefer text-based content and reflective learning. Adapting course design to the specific learner audience is crucial.

The Current State of MOOCs and Their Impact on Higher Education

Challenges and Opportunities:

MOOCs offer accessible education, but the current demographic is narrow, with early adopters of technology and some marginalized groups succeeding in MOOCs.

Platform Differences:

Platforms like MOOC.org and edX cater to schools beyond edX’s partner institutions. EdX and Google work together to merge platforms, but technical challenges remain.

The Future of Traditional College Degrees:

The traditional college degree model may change due to the rising importance of lifelong learning and retraining needs. The future of higher education may combine traditional colleges and online institutions, reflecting the dynamic nature of education.

Notes by: Random Access