Does mobile learning enhance your education? A review of Fulantelli, Taibi, and Arrigo’s “A framework to support educational decision making mobile learning.”

Fulantello, G., Taibi, D., & Arrigo, M. (2015). A framework to support education decision making mobile learning. Computers in Human Behavior, 47, 50-59.

In today’s society, more individuals are increasingly participating in informal learning settings, such as MOOCs, badged courses, Ted Talks, and from blog posts, such as mine. As a reader, you are exploring information channels expanding your personal learning networks, which I referenced last week (Blog post 9/25/15).  You may be engaging and interacting with other individuals on these sites, which naturally leads to deeper conversation and further participation.  You are increasing your knowledge about a particular topic or passion by participating in the online culture.  You are learning!  What a unique experience to be a part of, especially if you are from my era when digital learning was not available.  It is also exceptional is that this type of learning is reshaping the education landscape for students and teachers alike. For example, my 3^rd Grader uses a mobile tablet almost every day in class.  I didn’t have my first computer class until I was in high school.  This makes me wonder, are students learning more than we did because they have access to more digital information?  Are we measuring the impacts of informal and formal online learning with these new technologies?  How can we see what is occurring when students may not be in the traditional classroom setting?  I was tasked to find articles that related to learning analytics and I could help but explore this topic more by finding a scholarly article that tied to my questions. My article review is about researchers who wanted to explore this topic to see how mobile learning supports or hinders educational decision making for both our educators and educational systems.

My article review

Over the past decade, mobile learning (learning from a mobile device such as an iPhone) has been increasingly used to support learning experiences both in the formal and informal settings.  In the formal setting, Learning Management Systems (LMS) have developed apps, so that a student can download the app and access their online course through their mobile device.  In the informal setting, students are using Facebook or other social sites to engage in learning through discussions.  According to the research about 67% of students surveyed believe their mobile devices are important to their academic success and use their devices for academic activities.  The use of mobile technology allows for educators to develop innovative methods of learnings and policies aimed at participation.  Now, administrators and educators must look at learning analytics to optimize the learning process and guide teachers on how to enrich the educational settings.

Learning analytics is “the measurement, collection, analysis, and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which it occurs” (SoLAR, 2012).  Through learning analytics, educators are able to respond by using educational applications which are grounded in learning theories and understand the interactions between the learning and the learning context.  This research paper focuses on using learning analytics techniques to support educational decision making in mobile learning settings.  The researchers use a task-interaction framework, which is aimed to support teachers in assessing and evaluating learners during a learning experiences from their mobile devices as well as an agent-based interaction classification to gain additional insight for how to predict learners’ achievement in an online course.  The framework considers three main steps: 1) a real learning environment in which a planned learning activities which occur on a mobile device, which is the pedagogical model; 2) the analysis of the experience of learning activity; and 3) the undertaking of specific actions to modify the learning activities according to learning analytics also known as evidence-based indicators. 

The task-interaction framework to support educational decision-making in mobile learning used for this paper is based on the relationship between the different types of interactions occurring in a mobile learning activity and the tasks associated with the activity.  The framework evaluates six specific scale values which include context, tools, control, communication, subject, and objective.  It is important to identify the activities performed by the students and how they are associated with the six categories described.  The researchers focus on student-context interactions, which they evaluate the student relative to other students and their environment in which the learning takes place and the physical path the student followed.  Next, they looked at the tools used in the mobile learning experience.  The tools allow for content delivery to be controlled through prepared learning materials that the student consumes or through content construction where the student must create new content to demonstrate their knowledge.  Subsequent, the researchers looked at control of the classroom. This ranges from the full instructor control, by which the instructor guides the students towards learning goals, to full learner control, whereas the student is independent and learns without support.  Following this is communication. The communication between learners is important to assess in order to improve personal knowledge through cooperation.  The range of communication is from an isolated learner, where students only access course material and do not interact with the instructor or other students to cooperation, where students work together to achieve learning goals.  Next, it is important to consider the subjects.  The researchers need to know who are the subjects, are they novice or experts, in order to maximize the learning process. Finally, the researchers must know the objective factors to understand the student’s self-evaluation of how the student engaged in the content and context of the learning materials. 

All of these elements lead to the case study conducted by the researchers.  For the case study, they conducted two mobile learning experiments, which were designed to meet the learning objectives and pedagogical models of two different curriculums.  One experiment the students studied in the traditional classroom activities by visiting attraction sites around town.  The teacher provided the materials in the mobile environment and students worked individually and provided comments about each point of interest. The second experiment aimed at students collecting information about attractions around town to create a tourist guidebook. This was a collective effort amongst the students.  For this study, the researchers used a learning management platform called Mobile Environment for learning with Linked Open Data (MeLOD).  MeLOD was used to provide the students with contextual learning materials and make connections between the students and their experience with the context, other students, teachers, and learning materials.  MeLOD has the capabilities to analyze the information and create a dashboard as a visual tool to help teacher monitor students’ participation.  Using the dashboards, teachers were able to identify students who were not participating or over-participating in the class, students who were at risk of dropping out, how well the learning community was doing based on the students engagement, and notice any change in behavior after the teacher intervened.  All of this leads to a more dynamic learning environment and student success and knowledge about the course learning objectives.

In conclusion, the research study defined the main capabilities of why it is important to adopt a framework in the mobile learning setting in order to support educational decision making.  The advantages of adopting the framework include determining the relationship between the learner’s interactions and tasks within the mobile learning setting, supports the teacher in the analysis of how well or poor a student is performing relevant to a specific learning setting and define possible interventions to the curriculum or task-at-hand and offers new learning analytics that are beyond the numerical data.  Of course, there is room for further investigation to identify other areas in which this framework will work in other areas of mobile learning settings and scenarios.