Rob Kadel's article “Data-Drive Decision Making – Not Just a Buzz Word” discusses learning management systems and the characteristics required to make them a useful part of a dynamic classroom environment. First, Kadel defines a learning management system as “a type of software or webware that manages content and student data.” The essential question Kadel asks when considering the potential of a learning management system is “Who is my audience?” He indicates that the audience for such a system includes teachers, superintendents, and administrators. Interestingly, the students are the ultimate audience which will benefit from a system, though they are not mentioned as one of the audiences in Kadel's article. Kadel then discusses three functions to consider whether a system is flexible enough to meet the informational needs of all audiences.
Real-time reporting – Data being used to drive instructional decisions cannot be a year, a semester, or even a month old. Kadel gives two hypothetical examples of two different 9-year-old students – one who requires help in math multiplication tables and another who struggles with reading comprehension. The instructor can click on the average class scores to drill down on individual scores and ultimately get an item analysis to determine the areas in which the entire class is struggling. A school administrator can use such data to determine where the entire school is struggling or to determine which teachers might need some additional help with their classes.
Differentiation – Kadel indicates that “lack of time is perhaps the main reason that true differentiation of instruction has yet to be widely applied.” Ideally, to ease this time burden, a learning management system will have rules or algorithms to send an email to alert the instructor when either of these students needs remediation or perhaps a new style of exercises for learning.
Grouping for learning styles – Because students have different learning styles, a high quality learning management system allows for grouping students in particular ways based on their learning styles.
Finally, Kadel summarizes the the key points needed in a systems that provides data-driven decision making:
Analyzing the data collected
Identifying meaningful patterns
Alerting educators to learner challenges
Suggesting exercises to meet student needs
The learning management system that we use at Reading School District is Study Island. This year, we used Study Island in preparation for PSSA testing. Specifically, students were to complete one mathematics Study Island unit each week from mid-October to February. The data for the entire 11th grade class was then analyzed to determine the areas needing more targeted focus in the weeks directly prior to the PSSA tests. This strategy proved successful in the past for the PSSA Reading scores. We are still awaiting the Mathematics results this year to determine if our strategy proved similar results in PSSA Math.
Kadel, R. (2010, May). "data-driven decision making - not just a buzz word". Learning & Leading with Technology, 37, 18-21.