Read the case study and answer the questions that follow.

Data analytics is used in a variety of areas including business, healthcare, and education. Data analytics is defined as the process of investigating raw data of various types to uncover trends and correlations, and to answer specifically crafted questions. The process of data analytics includes descriptive analytics, predictive analytics, and prescriptive analytics. These areas of analytics are used to assist business in more informed decision making and allow them to better make sense of the large volumes of data created by their organization every day.

Students today dont learn in a linear way, which has created a disconnect between some learning strategies and student engagement and performance. Often (aside from tests and quizzes) it can be difficult to assess how well students know the information and concepts covered in traditional textbooks. For years, instructors had limited information on the time students were spending on reading and studying. Additionally, it was difficult to assess student and class performance. Another challenge was identifying specific areas where students demonstrated weaknesses or gaps in learning. These educational challenges lead to a need for better data for both students and instructors alike. The need to process data is true in many industries, including education. For this case study we will focus on the SmartBook 2.0 learning platform, how data analytics were used to drive platform design, and how data visualization is used to drive instruction and student learning.

McGraw Hill Education and Data Analytics-Driven Platforms

McGraw Hill Education was founded in 1888 by James H. McGraw and John A. Hill and, since the inception of the company, they have continued to develop learning solutions and textbooks to meet the everchanging needs of students and instructors across multiple disciplines. Currently, McGraw Hill works to integrate data analytics tools and techniques to leverage the benefits it provides across many learning platforms.

Two of the analytics-driven learning solutions offered are ALEKS PPL and SmartBook 2.0. McGraw Hill offers their ALEKS PPL (Placement, Preparation, and Learning) platform which provides actionable data that gives students and instructors visibility into placement details and learning progress. Visualization reports help instructors design courses that effectively meet the needs of students and their degree programs.¹

SmartBook 2.0 Using Data Analytics in Platform Design

Over the past decade McGraw Hill collected data from billions of questions answered across 90+ disciplines to study the impact of adaptive learning on student performance. This data was analyzed and used to create the framework for the SmartBook 2.0 learning platform. Key learning elements ascertained from data analysis that helped to drive design include the following:

the impact of metacognition on learning

the benefit of deliberate practice that allows students to focus on knowledge gaps and to maximize time on task

the instructional advantages of spaced instructional practice

Data analysis and analytics were used to create informed product design that allows for a better experience for students and instructors.

SmartBook 2.0 Data Visualization

Data visualization is the graphical and structured representation of data. Essentially, visualization makes it easier to see the results and conclusions ascertained from data analysis. When planning for data visualization it is important to consider how you will explore data sets for patterns, what data types are important to the end-user, and how the visualization of data can be optimally displayed and deployed.

McGraw Hill determined the types of data that can be collected from the adaptive learning activities and then sought feedback from key stakeholders including instructors, subject matter experts (SMEs), students, and product designers to determine the optimal mix of data to be visualized. This analysis led to the creation of data visualization dashboards from both the student and instructor sides.

Instructor Data Visualization from Adaptive Learning Activities

From the instructors perspective, one of the key benefits to using McGraw Hills SmartBook 2.0 adaptive learning assignments is the analytics and data visualizations that are provided after an assignment has been completed.

After students have completed an adaptive learning assignment, instructors can access a report/visualization of selected variables for the class and for the individual student including:

time spent on the assignment

a measure of a learner metacognition

and information on most challenging learning concepts covered in the assignment

This data allows instructors to create more informed instruction at a level that cannot be reached using traditional learning solutions. Instructors now have the ability to gain deep insights into student learning that can be used to create a more efficient and engaging instructional environment.

Student Data Visualization from Adaptive Learning Activities

From the students perspective, SmartBook 2.0 gives the student the ability to critically analyze performance in a number of different areas. Students are able to view the following:

their performance on key learning attributes for each adaptive learning assignment including assignment progress

time spent on the assignment

their level of metacognition

most challenging learning concepts

Additionally, each students performance is analyzed to create a customized Recharge of student learning. The Recharge feature in SmartBook 2.0 utilizes analytics to create a targeted review of concepts the student didnt perform well on or where they demonstrated a low level of metacognition.

Conclusion

In order to remain competitive and promote innovation, McGraw Hill will need to continue to innovate using the data and analytics provided from the SmartBook 2.0 platform. Continued discussions with key stakeholders and development teams will maintain McGraw Hills position as the market leader in data informed instruction and the application of analytics and visualization in higher education learning solutions.

Figure 1. Screenshot of visualization dashboard of class performance adaptive learning analytics provided by SmartBook 2.0.

Figure 2. Screenshot of individual student performance analytics provided by SmartBook 2.0.

Figure 4. Screenshot of student metacognition report provided by SmartBook 2.0.

Figure 4. Screenshot of Most Challenging Learning Concepts report provided SmartBook 2.0.

¹ https://www.mheducation.com/highered/aleksppl/data-analytics.html

The process of data analytics includes which of the following? check all that apply

A. descriptive analytics descriptive analytics

B. predictive analytics predictive analytics

C. prescriptive analytics prescriptive analytics

D. supportive analytics