The Differentiation quest explores the role of differentiation in an effective online learning environment and the concept of differentiation in the evaluation process.
The online classroom affords unparalleled opportunities to personalize teaching and learning. Data is available on nearly every activity of both the teacher and student. Therefore, an effective online teacher must learn to evaluate this data to adjust both instruction and student activities. Personalizing learning and differentiating instruction based on abilities is great teaching in any environment. Online learning allows for easier grouping of students and creation of unique learning pathways. This quest will equip you to identify how to differentiate for students and what precisely it means to do that.
The online classroom affords unparalleled opportunities to personalize teaching and learning. Data is available on nearly every activity of both the teacher and student. Therefore, an effective online teacher must learn to evaluate this data to adjust both instruction and student activities.
An effective teacher, irrespective of setting, reflects on the strength of previous lessons and improves areas of weakness prior to subsequent instruction. The online environment affords additional data to teachers to allow for more rapid and in-depth reflection than previously possible.
The image below is called a heatmap. This particular heatmap represents the activity within a Spanish course.
More specifically, the heatmap is a visual representation of all students and all grades in a course. The deep red boxes are high scores, and those that are closer to purple on the color scale are lower. Assignments are ordered from lowest overall class grades on the left to highest on the right. Not every LMS will have a report exactly like this but there will always be ways to evaluate through reports how well students did on assignments.
Now examine the areas of weakness.
The Spanish teacher can see that the module QU has more assignments on the low part of the spectrum than any others. Also, discussions and projects come up frequently as opposed to the exams that were on the high end. The students use the discussion to post audio files to practice speaking in this particular course, so the map indicates that the speaking component of the foreign language is not progressing well. Thus, the teacher may decide to focus more effort on speaking with students in Spanish, as well as refine how time is spent utilizing the language in lessons.
Data on student performance can also be used to group students for further instruction based on ability levels and even personalize the learning path each student takes. The example below comes from a high school economics course and the student results from the first module test. Click on the image to see a larger version of the example.
While the class as a whole did well, several students failed to demonstrate a strong performance. This report continues to document student progress, and the students who did poorly, such as 75 or lower, would require extra help before moving on to more material.
The teacher can set up additional resources in the LMS that are conditionally released to only appear for these students. Imagine that as soon as students finish the exam, they see a news item directing them to contact their teacher to set up tutoring, offering supplemental learning resources, and directing students to complete additional practice before proceeding to module two.
Moreover, the teacher can also complete an item analysis for each question on the test. Tests, in most LMS platforms, pull from a randomized question bank, so not every student will have the same question, but course with large enrollments will show results show easily. An item analysis will also highlight questions the class experienced difficulty with as well. Those questions that the class did poorly must be addressed as a class, in either a live online meeting or through extra instruction inside the LMS.
The International Society for Technology in Education (ISTE) shares an excerpt from the 2007 book, What Works in K-12 Online Learning by Cathy Cavanaugh and Robert Blomeyer. Titled Exceptional Learners: Differentiated Instruction Online (pdf file), the selection is pertinent to the topics in this quest and is worth reading.
Provide sample data from student results for a course within your field. The data can be entirely theoretical and written out in text form.
Upon completion of the data, discuss the method used to group students based on abilities, strategies utilized to examine student performance, and areas requiring modification of instruction.
Next, focus on the individual level. How could the data be referenced to identify the needs of each student? How could the settings of the LMS be used to create personalized learning paths? Note that a personalized learning path does not necessarily mean one student sees a resource no one else does, but that the overall order and selection of work one student does may be entirely unique from the order and assignments others did. Once the product is complete, share the results in your blog.
After completing a blog post that meets the requirements of this quest, submit the link to your post in the Submission Form at the bottom of this page.
Next, as a courtesy to fellow TOOL participants, join the Evaluate 3.1.1 Differentiation Forum and share the link to your post. Upon sharing the link with fellow TOOL participants, consider providing helpful feedback to others regarding this topic.
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*The submission of the Evaluate 3.1.1 activity marks the quest complete in TOOL, and the successful submission of all quests within the Evaluate skill will enable participants to self-award badges.