One of the many educational affordances of cloud-based computing is the ability to offer STEM students “hands on” opportunities through online laboratories. Both remote labs, which are real facilities operated through the Internet, or virtual labs, which are online simulations, can offer learning opportunities that go far beyond what might be available in even the best school labs — and many students around the world have no access to hands-on learning activities in any STEM subject. Integrating virtual and remote labs with an online learning course is the focus of a sister IEEE working group, IEEE P1876, Networked Smart Learning Objects for Online Laboratories. The goal is to provide each student an “individualized experience” of the lab activities. A collaborative effort with the Actionable Data Book project this summer used xAPI for both a summative and formative assessment data.
The learner’s unique laboratory experience is created by combining “Lab Learning Objects” that include class materials, examples, the lab activities, and the remote access to the experiments needed for that specific lab. This idea of a “Lab Learning Objects” is based on the concept of “Smart Adaptive Remote Laboratories for Education” proposed by Zapata-Rivera, Da Silva, and Petrie. It is inclusive of a Learning Management System (LMS/VLE) that passes information about the students’ progress in the class to the Remote Laboratory Management System (RLMS) . Based on that information plus a set of teacher-defined parameters, the RLMS adapts the interface “Lab Learning Objects” (Figure 1). The concept was prototyped as described in “Smart Adaptive Remote Laboratories for Education” which can be downloaded as a pdf.
Figure 1 Smart Adaptive Remote Laboratories for Education UML
Hamadou Saliah-Hassane, Chair of the IEEE P1876 working group, has been attending our monthly LTSC calls for a period of time and expressed a specific interest in John Costa’s ADB e-book project’s use of xAPI. Hamadou saw that xAPI could be used not just to report status and assessment data, but to actually integrate the LMS and LRMS systems — real-time feedback to both the instructor and student (and Moodle) about the student’s . Hamadou and John teamed up with Luis Felipe Zapata Rivera, co-author of “Smart Adaptive Remote Laboratories for Education” paper, who added the xAPI code to the remote laboratory system, a Traffic Light System with LEDs and servo motor.
In the integrated system, the remote lab reported xAPI’s actor-verb-object statements to its Learner Record Store (LRS). In the case illustrated in Figure 2, there are two students, A and B, reporting information about authentication, and every user interaction with the lab experiment. The ADB e-book incorporates the interface of the remote lab and, through the LRS, receives forwarded information about the remote lab (Figure 2’s Green shaded boxes).
Figure 2 Smart Adaptive Remote Laboratories for Education UML reporting information
Below are three screen captures of the statements that are sent from the remote lab to the LRS. The first one (Figure 3) is a general view of the messages received in the LRS, the second (Figure 4) shows the statement created when the student save the python code in the editor, and the third one (Figure 5) is a statement created when the user runs the code in the remote experiment. Additionally, the statements can be forwarded to other LRSs.
Figure 3 Smart Adaptive Remote Laboratories for Education xAPI Statement to the LRS
Figure 4 Smart Adaptive Remote Laboratories for Education xAPI statement created when the Student A runs the code in the remote experiment
Figure 5 Smart Adaptive Remote Laboratories for Education xAPI statement created when the Student A saves the python code