Chapter 4: User Need Analysis through User Research Studies
5.4 Facilitating teaching through SLS using an IOT approach: A conceptualization
Figure 5.19 Block diagram representing increasing degrees of intelligence embedded into the learning system.
It is important to note that such systems have distributed intelligence, i.e., an object embedded with specific computational capability alone cannot act like a complete me- dium for learning. It is through multiple modes that it interacts and provides necessary instructions and experience to students. Our SLS prototype is based on this rationale.
When we further consider how an SLS can be upscaled to further cater to the need of instructors and students on a large scale, the potential of IoT can be leveraged. In the following Section 5.6, we describe this conceptual scenario.
5.4 Facilitating teaching through SLS using an IOT approach: A conceptu-
learning environment in practical laboratories that can be possibly implemented on IOT approach. However, these studies mostly focus on understanding the context of the user regarding their physiological or location-based context.
In a conventional laboratory environment, instructors obtain feedback on student learning experiences in face-to-face interactions with students. This helps them in con- tinual evaluation of their teaching methods. However, in case of a large batch of labora- tory sessions, this face-to-face interaction often gets limited to very few student groups as the instructors often need to spend much time with small debugging issues in one group. This often leaves few other groups, that require more assistance of instructors, waiting in queue for long time durations.
Sometimes, these groups are not able to receive the attention of their instructor at all in a time restricted laboratory session. This causes a burden on the instructors in the next practical session to help the lagging group to catch up with the rest of the class. To overcome this difficulty, we conceptualized the following model to help instructors get assistance and feedback on teaching. The following paragraphs describe how Educa- tional Data Mining (EDM) used with SLS can help tackle these difficulties.
We present a conceptualization of IOT scenario based on the approach of inter- connected smart devices and applications. The concept derives its understanding from the field of EDM that relies on user-interactions with a learning portal (mostly web and MOOCs based) to create various interaction and time logs (Khasawneh, Box, & Chan, 2006; Romero & Ventura, 2007). These logs can be analyzed to inform the instructors to tailor-suit the content to individual students needs to improve their overall learning ex- perience. These logs also help instructors monitor and assess their teaching methods. We extend this concept for use into our designed SLS prototypical solution that utilizes AR and intelligent breadboard based smart objects.
Figure 5.20 depicts a conceptualized model of integrating IoT for practical elec- tronics laboratory along with SLS.
In case of our AR application prototype, the user interacts with the application via AR view output modality. We propose to use an interaction logger (see Figure 5.21) with this modality to track how users interact with the content of AR application and for how long does a user interacts with the content. This log can help instructors understand how users interact with information to understand an experiment. If a group of such logs gen- erated from a number of student users using AR application is presented to the instructors visually, it can help them discover interesting patterns that can help in continual assis- tance and evaluation of groups. The instructor will be able to understand what type of information is being accessed most by students in lab sessions. How much time is the being spent by students to consume this information? Perhaps the information being accessed most requires more attention thereby requiring more tutoring and pre-lab ses- sions. This information log can be presented to instructors visually through a digital tablet interface to help them get a better visualization of class behaviors. This will be elaborated soon in a few paragraphs to come,
As a proof of concept, we developed a simple model to count the number of taps (user interaction in the form of clicks) on individual 3D elements of our AR application prototype and then send this information from smartphone (loaded with our AR applica- tion) to a web-based interface. Figure 5.21 presents screenshots of this demonstration.
(a) (b) (c)
Figure 5.21 Screenshots from a simple demonstration of generating user-interaction log via AR. (a) User interacting 3D element on screen via AR view, see the arrow pointing towards a white circle. (b) A counter keeps track of the number of clicks on individual 3D and the graphical element of AR application and stores them as a log file in text format. (c) This log file can be sent to a web-based interface and presented to instruc-
tors visually
For intelligent breadboard, the user utilizes both input/ sensing layer and interac- tion layer for interaction. In the input/ sensing layer, we add an error logger and an inter- action logger at the interaction layer. Error logger can keep track of all the mistake that
occurs while prototyping the physical circuit on a breadboard and categorize them into different types of mistakes as discussed in Table 5.3 of Section 5.3.1.
All the logs are sent to a centralized server or “the cloud.” This is where the po- tential for AI comes into the picture. The cloud contains a massive data on the types of mistakes that commonly happen for particular experiments in practical lab session and the type of information accessed by the students in that practical experiment. Is there a certain category of mistakes and information that is being used by the student? Are these students categorized based on regions, colleges, academic performance? If so, what is the most suitable type of instructions that can be provided to these students? What rec- ommendations should be given to the instructors? These are some of many questions that need to be answered by AI. The AI’s task is to find a suitable type of instructions based on the logs sent by the devices. It should be able to predict the right type of instruction and recommendations for both students and instructors for augmenting learning and teaching experiences in the complex learning environment of a practical laboratory ses- sion. This segment of our thesis is a doorway for future work in this area.
We now explain the concept of an instructor’s dashboard on a digital tablet for as- sisting the teaching of student groups in labs.
Instructor interface
This section briefly discusses the conceptual interface for laboratory instructors.
The aim of this interface is to act as an information platform that provides real-time feed- back to laboratory instructor regarding students’ performance and difficulties. This plat- form can be used by instructors to aid them in teaching large batches of students.
Based on the observations from practical electronics laboratory sessions, various activates in which instructors are involved in were observed – which mainly consist of assisting students, accessing their progress and evaluating them. It was observed that although instructors play a crucial part in laboratories to give guidance and feedback to students, no mechanism does the same for instructors. With our proposed conceptual scenario of integrating SLS with IOT and AI, it is possible to develop a platform for instructors that can provide them with timely feedback and ability to monitor a large
Various techniques such as experience mapping, paper-prototypes and wireframes were utilized, see Figure 5.23. A dashboard was designed as a concept to show how in- structors can get assistance through the use of AR and SO in an IoT scenario. The com- plete interaction flow diagram of the dashboard has been presented in Annexure E3. The dashboard, shown in 5.23 (c), presents information that is required by instructors to assist and monitor students’ progress and activities related to practical experiment. These in- clude, students’ assignment status, progress in class, top errors or mistakes made by them in experiments and completion status of experiments. The instructor can either choose to view individual progress of the students or group-wise progress. Top errors and mistakes section helps instructor get feedback on the most commonly occurring errors/ mistakes by students while conducting the experiment. Based on this information, the instructor can decide upon the type of instructions to be given to students. The interface assists instructors in identifying students who are lagging or facing difficulties in performing the experiment.
Figure 5.22 Steps followed for the conceptualization of instructor interface.