Hands-On Learning exhibited and presented at the 8th Annual Emerging Technologies for Online Learning International Symposium, April 22-24 in Dallas.
The information session was titled, “The Data-Driven Classroom – This Ain’t Your Daddy’s Data.” Below is a summary:
New technology in education is all the rage. Today’s technology tools collect quantitative information that drives innovative pedagogies and ramps student engagement and learning outcomes. Classroom data helps instructors work smarter, not harder. But which technology delivers the data you want?
Online courses lend themselves to data-driven instruction. Student knowledge gain can be continuously tracked, classroom analytics can become the root of action, and data can be mined to predict trends in the greater student population.
Tracking Student Knowledge Gain
How do you identify when learning actually occurs? The first step is to create precise learning objectives that can be measured. Learning objectives that begin with vague terms like “understand” and “learn about” are nearly impossible to track with analytics because they are subjective and are not measureable. How could we ever truly measure a student’s understanding of a subject such as photosynthesis? Generalized course-level objectives are too broad to track. So how do you create definitive learning objectives that are well-suited for data collection and point to a specific expectation that can be measured through assessment?
Assessments are the cornerstone of the data-driven classroom. They must be placed at key moments throughout the learning pathway. Where should they be placed to capture key data? What are the different types of assessments? What is the difference between formative and summative assessments? It is important that a variety of evaluations be presented as learning progresses. Student knowledge gain is tracked by identifying a single learning objective and aggregating student data from assessments, which are highly engaging and great for test skill-building. Data about student performance can be continually collected and subsequently applied in a number of ways.
Applying Actionable Analytics
The data-driven classroom is built with meaningful analytics that initiate action and have predictive value. Actions may be taken at the student-level or the classroom-level. How do you create measureable milestones? When do you implement Just-in-Time Teaching (JiTT)? With actionable analytics, instructors can quickly identify a student who performs poorly on an introductory topic and provide help or an engaging resource. Learning opportunities are recognized at the moment needed, maximizing the potential for student success. How does adaptive learning contribute to this process?
Data can transform our assumptions and understanding of student knowledge. For example, recent analytics collected on a “Laboratory Techniques and Measurements” learning module indicated massive student success for performing molar calculations but very marginal success on describing the proper use of a graduated cylinder. This confounded the expectations of educators, who anticipated student performance on math-related topics to be the challenge area. Without the assessment data, the educators would have continued to build instructional resources around math. However, with the assessment data, instructors were able to focus their efforts on an important knowledge gap. Analytics allowed the instructors to work smarter, not harder and educational effectiveness was improved.
Predictive Analytics and Big Data
There are endless possibilities in the application of analytics, and the educational market is only now scratching the surface of these applications. How does your classroom fit in with university-wide data? Analytics can be used to gauge students’ own opinions of engagement and perceptions of knowledge gain, and these too can be correlated with student performance. Classroom analytics can be used to inform department-wide approaches and help institutions develop instructional best practices in topic areas. Student performance in introductory classes can be applied toward big data and utilized as predictive analytics for future student success. But most importantly, analytics provide a vehicle to move education away from hypothetical theory, towards pedagogical models that are supported by empirical evidence. Through online resources, instructors are able to generate data about teaching effectiveness and provide support for novel approaches. In many ways, student performance data is able to validate best teaching practices as it never could before. The online environment is the ideal setting for a data-driven approach, and online instructors, who admittedly are the most adventurous and innovative group of educators, are well-suited to the task of revolutionizing education.
HOL also presented a session during the Vendor Showcase entitled, “Yes, You Can Teach Science Online!” The session highlighted how Hands-On Labs has integrated technology with hands-on laboratory experiences to achieve better learning outcomes than many face-to-face classrooms.