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Leveraging the LMS

Posted by Eric Ludwig at 6/15/2016 10:05:25 AM

The learning management system is inherently limited.  The LMS is a closed system -- i.e., it does not interact naturally with the broader online and digital world.  It requires specific programming and technical specifications to make interactions occur between external programs and itself (like an API, or LTI).  Though a number of learning management systems are now open source, this has had little impact on the overall user experience and underlying function of the LMS.  There are still broader questions we should ask about the centrality of the LMS to learning and how it might constrain our ideas of what online and blended learning can be.  For more on this topic, I encourage you to read Michael Feldstein’s (2015) excellent four-part series on what comes (or should come) after the LMS and Malcolm Brown, Joanne Dehoney, and Nancy Millichap’s (2015) piece on the future of the LMS in Educause Review.

As for now, the vast majority of institutions are operating within the LMS-world.  That world has a finite number of choices.  Despite all of its limitations, the LMS can still be an incredibly powerful tool to deliver learning to students across space and time.  And with that learning comes data that tells us a detailed story about what, how, and when our students are learning. 

Given the ubiquity of the LMS, it should not come as a surprise then that there is an abundance of data available to instructors, staff, and administrators.  Learning analytics -- just a fancy term for the data that we can cull from the LMS, enterprise systems, or other digital learning platforms -- provide information that can be incredibly useful when trying to predict student performance and persistence.  In a recent post on Inside Higher Ed, Paul Fain gives a brief overview of how certain institutions -- with help from external consultants -- are incorporating the data from the LMS into their retention models.  Information gleaned from LMS data can then be used to identify students who are at risk of dropping out or failing.  This is often done in concert with an early alert system for academic support and advising.  

As Fain notes, institutions have traditionally relied on other sources of information (e.g., GPA, midterm grades, advisor meetings, demographic data) to alert them to at-risk students.  However, data pulled from the LMS can offer a more accurate and timely picture of student engagement.  An end-of-semester GPA occurs at the end of the semester: it might suggest future student performance, or it might tell us that a student has already failed a course.  Demographic data fails to tell individual student stories and experiences.  A student often requests to meet with an advisor after an event or experience has impacted their success and ability to persist.  

Fain contends that student GPAs “lag in comparison to engagement data as a predictor”.  We might expand on his use of the verb “lag” to include the economics term “lagging indicator”: a lagging indicator describes a “measurable economic factor that changes after the economy has already begun to follow a particular pattern or trend.”  These descriptive student metrics tend to tell us the story after it is already in motion.  The LMS data can tell us the story in real time. 

But, the LMS is only as powerful as its users.  For many faculty, staff, and students, the LMS still seems far too complex or confusing.  Sparse or intermittent LMS usage might lead to unreliable or inconsistent data.  In order to strengthen that signal and leverage LMS data to improve student outcomes, institutions should work on the following:

  1. Robust and regular LMS usage: train, support, and encourage all faculty to utilize the LMS regardless of learning modality.  Whether face-to-face or online, students will use the LMS only to the extent that their instructor requires it.  Limited interaction means limited data.

  2. Systems integration:  the LMS should be seamlessly integrated with other enterprise and assessment systems for easy data tracking and reporting.

  3. Modeling & Early Alert: LMS data should be incorporated into retention modeling and early alert systems

  4. Staff training:  advisors and academic staff should be able to analyze LMS data and be trained to spot indicators for at-risk students

What is alignment?

Posted by Christine Dereberry at 6/6/2016 10:06:34 AM

Imagine you are planning a camping trip. You’ve decided you want to camp for four days in Traverse City, Michigan to see the sand dunes. You’ve made an extensive list of supplies for the trip based on camping internet sites/camping books/and advice of your friends on what is needed for the trip and encompasses everything from tents, bedding, food, and clothing. Included in the planning was a map to Traverse City and an itemized list of activities, reservations for camping and places for sightseeing. The goal of the vacation is to enjoy a day driving dune buggies on the sand dunes. You are excited for a fun 4th of July weekend in Traverse City, Michigan.  After all the careful planning you ended up in a hotel in St. Paul, Minnesota and eating at a steak restaurant. What happened? The answer is a question of alignment.

If we take apart the planned vacation in terms familiar to those in education you end up with four stages in the vacation planning that weren’t aligned:

  • The goal of driving dune buggies on the sand dunes in Traverse City, Michigan was the objective of the trip.

  • Successfully making it to Traverse City, Michigan and driving dune buggies on the beach during the camping trip is the final assessment.

  • The research of sites/books about camping and the list of supplies needed for the trip represent the instructional materials.

  • Creating the map and the itemized list of sightseeing locations, reservations and activities are the learning activities needed to make the trip.

By ending up in St. Paul, Minnesota as the final destination it is apparent that the four stages of the trip were not in alignment; the objective, the instructional materials, learning activities, and assessment. Upon further investigation you realize the map you used was not to Traverse City but to St. Paul. In addition, the resources used to create the list of supplies were based on websites and books about St. Paul.

While the story above uses an analogy to make a point, issues with alignment of course objectives, assessments, instructional materials, and learner activities due occur. Misalignment of these four key course components is detrimental to the success of the learner reaching the stated objectives of the course.

The course design process begins with crafting the course objectives followed by outlining and creating the course assessment. When the objectives and the assessment are in alignment, the instructor has a clear picture of what they want the student to achieve after the course is over. 

Next, the instructor must locate the instructional materials that the students will consume in order to learn the course content which may include textbooks, PowerPoints, PDFs, videos, podcasts, instructor voiceover lectures, checklists, templates, and web content. The learner activities are then created, which utilize the instructional materials so that the progression through the activities will lead the student to create new knowledge, skills, meaning, and application of the new content. Lastly, as the learner activities are created, the instructor must state which objectives are linked to each learner activity and state how the activities and instructional materials will help the learner achieve the stated course outcomes and success on the assessment.

Alignment of these four parts of the course design are essential to the student’s success in learning the course content. For further assistance with course design, please contact instructionaldesign@stritch.edu