Understanding heutagogy during a pandemic: A case of Universitas Indonesia

Abstract The COVID‐19 pandemic has not stopped the academic world. Since mid‐March of 2020, lecturers at Universitas Indonesia have facilitated the learning process by providing teaching resources as well as guidance through teleconferencing platforms. This study shows the importance of self‐tracking in academia, especially for lecturers. Our findings suggest approaches to measure lecturers’ performance levels through a quiz and questions raised to understand students’ behaviors.


| INTRODUCTION
The COVID-19 pandemic has not stopped the teaching and learning throughout the world. A plethora of teleconferencing platforms has provided alternative solutions to the in-person teaching process (Gon & Rawekar, 2017). During this challenging time, professors and students at Universitas Indonesia (UI) have been transitioning from face-to-face study to distance learning since mid-March of 2020. From the beginning, however, various problems began to arise. For instance, although students could access their courses through the university's online platform from their homes, they complained that the university failed to provide internet quota packages.
As a result of the pandemic, researchers have striven to understand online learning based on students' experiences and, in some cases, have employed a rapid uptake multimodal teaching approach. Through this study, we aim to discover the relationship between lecturer performance and student behavior. The specific research questions are as follows: 1. How did schools manage online classes in the early days of the pandemic?
2. What will a quiz and student questions reveal regarding the relationship between lecturer performance and student behavior?

| RESEARCH DESIGNS
In March of 2020, after midterms, the author (as a lecturer) was responsible for seven remaining meetings across four courses. The lecturer then needed to optimize a combination of teleconferencing platforms. For example, the instructor would use Zoom only when inviting a guest lecturer to the class but would turn to WhatsApp group for written discussions. Before examining the relationship between lecturer performance and student behavior through a quiz and student questions, the lecturer gave a short-written assignment to three of four classes. The assignment concerned the relationship between the course and the COVID-19 pandemic. The lecturer then compiled the students' writings so that the assignment could be assessed by the other students. They all had to provide the form of reasoning-objective, subjective, objective and subjective, or another form of reasoning-they used to select the scores they had given to their peers. The lecturer analyzed the peer assessments as a means of recognizing students' behavior (see Figure 1). Finally, this method provided important insight into student agency in learning (McKeachie & Svinicki, 2013).

| Quiz results
The lecturer was responsible for teaching four courses: (a) Course A was attended by 29 students of various disciplines; (b) Course B was the internal court for the class of 2017 with 17 registered students; and 3-4) Course C and Course D were the same internal courts divided into two classes for the class of 2019, with 29 registered students per class. Figure 2 shows an example of peer assessment distribution given in Course C. In this instance, 29 students from Courses C stated that they graded themselves and other students objectively (n = 17), subjectively (n = 8), subjectively and objectively (n = 3), and based on other reasons (n = 1). The average results of the assessment were analyzed using inter-item correlation, an essential statistical method for conducting item analysis within a set of scores (Piedmont, 2014). Table 1 shows a peer assessment example of a correlation matrix given by all students to other students, in which a number below 0.3 indicates a weak correlation. For example, student A has a strong relationship with student B but a weak relationship with student C. The results also recommend increasing awareness of relationships among students. This matrix, however, does not show causality.
The successful triangulation of the results was confirmed by a student in Course C, who stated that "[t]he results of the inter-item correlation matrix are indeed in accordance with the reality of daily friendships. I cannot believe the numbers can predict this correctly." The validation of this student demonstrates that the results of this study may help lecturers by identifying and paying attention to the students with weaker interpersonal relationships. Bititci, Garengo, Dörfler, and Mendibil (2009) recommend measuring performance by asking questions. Marr (2010) developed the concept of Key Performance Questions to provide guidelines for collecting meaningful performance indicators. In addition, this study analyzed lecturer performance by counting the number of students who raised questions (questioners) from each course through written discussions on the WhatsApp group platform. The raised questions were used not only to evaluate lecturer performance but also to indicate future trends in online courses. Figure 3 shows the total distribution of questioners per course.

| Evaluating lecturers' performance
The highest average number is seen in Course A, followed by Course C, B, and then D. The average number of questioners in the four courses was six (Mean = 6.22). Our findings also show that the start time of a class affects the level of student participation in written discussions on the WhatsApp platform; for example, Course C started at 8:30 a.m., whereas Courses A, B, and D started at 1:00 p.m. Figure 4 also shows the average questioners sorted by a gender binary based on the total distribution of questioners per course (see Figure 3).
Furthermore, students can also evaluate the performance of lecturers through a platform provided by the university after the completion of teaching process. Sixteen questions on a scale of 1-6 that must be answered by each student. Course A has the highest overall average rating, followed by Course C, D, and B. This finding identifies a relation between lecturer performance and student behavior.
One participant noted a lecturer constraint: "I want [ed] to ask [a question] in the WhatsApp group, but I was ashamed to ask because I had made a mistake when I was in another WhatsApp group with another lecturer. I apologize that I was not active in the group due to the incident." (Female, Course D).
Another recognized a few tool constraints: "To be honest, this distance learning makes me lazy, especially when I know several lecturers give many assignments with fast deadlines, and there are obstacles because my laptop is being serviced but thank God I can still use a smartphone. Sometimes these constraints make me lazy and feel I do not care about the task." (Male, Course B).
Other constraints were also identified from the example above, in addition to this female student's fear of participation and this male student's worry about laziness.

| CONCLUSION
Self-tracking performed by the author to measure students, as well as the lecturer's participation in the teaching process, are important when evaluating remote teaching and learning contexts that have emerged globally due to the COVID-19 pandemic. The approach used in this study to develop students' learning abilities was a proactive one, and students served as the primary agents in their own learning, F I G U R E 4 Average questioners sorted by a gender binary which is a process referred to as heutagogy (Canning & Callan, 2010). This paper also triggers academic continuity efforts in defining the presented problems and in creating innovative solutions for post COVID-19 stage.