Chapter 4: User Need Analysis through User Research Studies
5.3 Design Heuristics for Embedding Intelligence
Mapping Errors and Instructions to Experimental Task-Flow
Through interaction analysis, (discussed in Chapter 4: Section 4.2.2), laboratory ac- tivity of students for experimenting has been broadly categorized into four steps (refer to Figure 5.16).
Figure 5.16 Block diagram of students' activity in a practical lab session. Assembling and operating test equipment form a significant part of this process.
It is during assembling and operating test equipment stages that significant errors or mistakes are made by students that increases their effort, mental demand, and frustration levels. As observed from videos, interviews, think-aloud sessions and open-ended ques- tionnaire, such errors can be classified into four categories. These are – (i) Physical er- rors, (ii) Perceptual errors, (iii) Theoretical errors and (iv) Technical errors. Physical er- rors and perceptual errors can partly be categorized under the interactional errors, i.e., errors that happen while interacting with the experimental setup. While not all physical errors contribute to mistakes made by students (e.g., loose connections), wrong connec- tions or placement of components does. Perceptual errors are mostly visual. These can occur during any of the four stages of experimental activity. However, while conducting a practical experiment, they happen when students visually misperceive connections or components. Table 5.3 describes these errors mapped to students’ activity in a practical electronics lab.
Table 5.3 Types of errors made by students while doing practical experiments mapped to the activity.
Type of Error Description Activity
Physical Break in connection not visible to human eye, Loose Connections,
Wrong arrangements of components.
Assembling, OTE
Perceptual (Visual Perception)
Wrong Connection,
Use of faulty electronic component.
Referencing, Assembling, OTE, Reporting
Theoretical Wrong understanding regarding electronic component, test equipment or experiment.
Referencing, Assembling Reporting
Technical Faulty lab equipment and objects Assembling, OTE
Figure 5.17 below depicts various modes of interaction and different modalities through which it is possible to address various difficulties experienced during experi- menting phase.
Figure 5.17 Guidelines for embedding intelligence through various modes on the interaction of AR, smart objects, and product interfaces.
We posited that if a system can guide students through this process while mini- mizing the number of trial and error efforts required in debugging circuits or operating
test instruments, an effective learning experience can be created for students. Such a sys- tem would then be capable of reducing the amount of frustration and cognitive load of students.
Studies (Sweller et al., 1998; Van Gog & Paas, 2008) from cognitive science show that for knowledge assimilation and schema formation to take place, cognitive load experienced by students should be minimal. Hence, developing physically intelligent agents capable of embodying highly structured information and instructions can help stu- dents learn effectively in a lab. The basis of this statement sprouts from work on AR (Yuen et al., 2011), tangible interaction (O’Malley et al., 2004) and human ideation (Oviatt et al., 2012) that highlight the importance of physicality, natural affordance and interface of objects. As reported in the interviews, a major concern for course instructors in these labs is to be able to relate theoretical aspects of the experiment to practical, real- life applications. For this is it necessary to provide highly situated and contextualized instructions for students which can help them relate to such concepts. Hence, there is a need to generate suitable instructional algorithms that embody tacit knowledge of in- structors which is highly structured in nature. For doing so, Task flow diagrams (TFD) were created (refer to Figure 5.7, Section 5.2.1) from think-aloud sessions and observa- tional studies for experiments reported to be difficult for students. These TFDs high- lighted various instructions that should be generated when a specific type of error is encountered, and that invoked inquiry-based learning in students during various steps of the experiment. The instructions were captured from interviews of lab instructors. Figure 5.18 describes the process of collating this database through a UCD approach.
Through UCD approach, we can capture a large number of user experiences in labora- tory session that can be used to augment the learning of students. These experiences can be collated into a database, which can be used for such a purpose.
Defining Intelligent Systems
The complete experimental setup usually consists of various objects and equip- ment in labs. Students while working with this setup interact with various objects in this scenario and also distribute a part of their intelligence into those tools (Salomon, Perkins,
& Globerson, 1991). Mapping errors to the task-flows help in cataloging the types of interactions that are likely to happen between the experimental setup and students. By identifying objects that are used mostly by students and have a high likelihood of pro- ducing interactional errors can be chosen to be embedded with computational capabilities to sense such mistakes. Doing so creates a tangible user interface (TUI) that acts as an input mechanism for error and task sensing. From such system, further developments can be made to define what types of instructions are most suitable for students depending upon the state sensed by TUI.
To adequately convey these instructions to students, various types of output mo- dalities can be defined. These have to be chosen such that students get the most out of their learning experience (in this thesis we utilize AR, smart objects, voice, and text- based interaction modalities.). Hence, as we move towards increasing the experiential learning value, we are also tending towards increasing the Degrees of Intelligence (DOI) to be embedded into our tutoring system.
Figure 5.19 represents a block diagram of the proposed model of DOI that con- veys how intelligence is being embedded into the objects which when used in combina- tion or with other equipment and instruments in a lab, form a smart learning system (SLS) for students in the lab. The sensing layer contributes towards a first degree of intelligence (1-DOI) and is mainly responsible for sensing and computing functions (for example intelligent breadboard senses user’s errors or mistakes during physical circuit prototyp- ing). Developing adequate instructions and learning content corresponding to task-flows and errors is the second degree of intelligence (2-DOI). Designing rich learning experi- ence and interactions with the system is a third degree of intelligence (3-DOI) and in this thesis has been achieved by utilizing AR as interaction and visualization modalities.
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-