Conference Agenda

A flipped classroom is a blended instructional approach in which the in-class lecture is shifted to before-class learning using videos or other forms of online learning to free up in-class time for students to discuss issues, practise, or apply knowledge (Bergmann & Sams, 2014). A persistent problem with implementing flipped classroom is that, without proper guidance or assistance, students lacked the engagement and self-regulation skills to complete online learning activities before class, and hence failed to learn effectively in the following in-class lessons (Mason et al., 2013).

In an online learning environment, promoting students’ self-regulated learning (SRL) through planning, goal setting, organising, self-monitoring and self-evaluating during the learning process is imperative (Zimmerman, 1990). The theoretical framework of SRL is underpinned by three components of SDL identified as: (1) metacognitive strategies; (2) task management and control; and (3) cognitive strategies to learn the materials (Pintrich & DeGroot, 1990). Previous research has reported that motivational beliefs are positively associated with SRL (Credé & Phillips, 2011).

With the advancement of Artificial Intelligence (AI) technology, chatbot has gained prominence in education and regarded as a useful tool to provide personalised guidance, support or feedback to support students’ learning. There is a growing body of evidence on the use of chatbots to promote students’ SRL in an online environment (Du & Hew, 2021; Hew et al., 2022; Song & Kim, 2020). Other previous studies have reported that the use of chatbot-based learning have contributed to higher students’ learning achievements, self-efficacy, learning attitude (Lee et al., 2022), intrinsic motivation (Yin et al., 2021) and critical thinking (Chang et al., 2022).

In recent years, scholars have highlighted that there is a lack of studies to investigate the effectiveness of learning designs or learning strategies using chatbots (Chen et al., 2020). To date, there is a paucity of studies on chatbot-based learning which investigates the motivational factors that influence students’ SRL in the flipped classroom, particularly with using a critical thinking module and mixed-methods research methodology. Therefore, this present study is an attempt to address this gap in the literature by employing a chatbot combining with worksheet scaffold to investigate the extent to which motivational factors can influence students’ SRL before-class. Understanding the motivational factors that impact students’ SRL using chatbots in the flipped classroom is crucial for researchers and educators to reflect upon, and develop better learning approaches or interventions to support students’ learning in the future.

Specifically, we formulated the following research questions to guide in the data analysis:

RQ1: To what extent does the four motivational variables (i.e. intrinsic goal orientation, extrinsic goal orientation, task value and self-efficacy) correlate with SRL among students using chatbots?;

RQ2: To what extent do the four motivational variables (intrinsic goal orientation, extrinsic goal orientation, task value and self-efficacy) predict SRL for students using chatbots?;

RQ3: Based on the interviews, what are the students’ perspectives of their learning experiences in using chatbots?; and

RQ4: In what ways do the interview data reporting students’ perspectives of their learning experiences in using chatbots help to explain the quantitative results in the online questionnaire?

Immersive virtual reality (VR) is increasingly used as a tool for vocational training, especially safety critical vocations such as mine rescuers’ safety and aviation safety where real-world training is often too complicated, costly, or risky (Pedram et al, 2020, Buttussi & Chittaro, 2017). VR has triggered innovative changes in education by allowing learners to acquire skills through repetition and practice in virtual learning systems. VR can provide real-time visualisation and interaction within a virtual world that closely resembles a real world (Chuan et al, 2018). This allows students to practise procedural skills and partake in decision making in an authentic and safe environment.

Very often, these VR learning environments require the use of head-mounted devices which are costly, fragile and not suitable for use over extended training sessions. Henceforth, the development of low-cost, high-resolution personal computers has made it feasible to offer 3D VR learning in school settings through desktop VR learning systems (Huang et al., 2016).

The delivery of VR through head-mounted devices are used in most VR systems to create a sense of immersion for users. On the other hand, desktop VR simulates a real environment or 3D representation of an abstract concept on the screen, which allows learners to interact with the virtual environment using a keyboard mouse, or other navigation/control devices (Merchant et al., 2012). Head-mounted VR offers a more immersive learning experience but the provision of head-mounted devices for every student is a challenge, especially for big classrooms and home-based learning. VR made accessible on any PC or laptop gives students the opportunity to practice and gain mastery of air traffic control skills at their own pace. This could be useful in vocations such as air traffic control where costly equipment are needed to train learners in procedural knowledge and skills.

To date, there are limited studies on the effects of different types of displays on procedural knowledge and skills acquisition in air traffic control. In a study conducted on the effects of different types of virtual reality display on presence and learning in an aircraft safety training scenario (Buttussi & Chittaro, 2017), the types of VR display (desktop VR vs head-mounted VR with different field of view and degree of freedom) affected users’ sense of presence but did not significantly affect self-efficacy. According to Makransky and Lilleholt (2018), immersive VR provided significantly higher presence than desktop VR, a strong positive predictor of both motivation and enjoyment, which in turn positively predicted perceived learning effectiveness.

Hence, this study aims to compare differences in perceived learning effectiveness between VR Head-mounted Display (VR-HMD) and VR Desktop Display (VR-DD) for procedural training of air traffic control. In addition, the study examines the extent to which the variables, i.e. perceived ease of use, perceived usefulness and sense of presence will influence students’ perceived learning when using different VR displays.

The following research questions were formulated to guide the data analysis in this study:

1. To what extent does each predictor variable (perceived ease of use, perceived usefulness, sense of presence) correlate with perceived learning?

2. Are there significant differences on perceived ease of use, perceived usefulness, sense of presence and perceived learning between VR-HMD and VR-DD?

3. To what extent do perceived ease of use, perceived usefulness and sense of presence significantly predict perceived learning between VR-HMD and VR-DD?

Chatbots are tools that have been developed to improve the machine-human interface for better communication and interaction as well as for more efficient and cost-reducing services. They are well known in and for online business approaches. Its use has been started and is discussed in education as well, but the application is sometimes seen as a potential or even a threat that might compromise proper learning opportunities for students. One might see similar and additional effects with the introduction of digital media seen as an environmental condition, where one can recognize that the average IQ of students has decreased in the last years (Bratsberg & Rogeberg, 2018). Another main concern is the risk to open the backdoor for plagiarism by developing text in learning contexts by students themselves. However, reviews (Okonkwo & Ade-Ibijola, 2021, Pérez & Daradoumis & Puig 2020, Cunningham-Nelson et al., 2019) on chatbots/AI applications in education called edubots show the variation of and the potential quality of its use in the educational sector. Efforts and initiatives are on their way to improve their architecture for educational purposes (Sjøstrøm et al., 2018).

Whereas applications for students are implemented and under research for instance in Learning Management Systems (Lee et al., 2020) to assist students learning, applications for teachers specifically on assessment are less in focus with some exceptions. Just 6 % of the edubots support assessment activities (Okonkwo & Ade-Ibijola, 2021, p.5-6). An eclective literature search in Oria (Norwegian electronic library tool) used here as an example of the research and publication distribution has shown that the search on ‘edubots’ resulted in 93 hits, whereas just 19 were related to ‘edubots & teachers’ as well and just 11 hits on the search topics of ‘edubots, teachers, and assessment’.

Roots of the edubots for teachers one can see in the tutorial systems (TS) in the 1960 (Opwis, 2001) which have later become intelligent, supported by AI called ITS. At that time computerized assessment was an equal part of it, but rather or mostly from a quantitative point of view.

Therefore, this paper will focus on the potential to support better qualitative assessment which lies in the learning potential of written feedback. This is not meant to replace the teachers’ educational autonomy and duty in doing assessment tasks him/herself. The intention is to reduce the workload of routine tasks in assessment whereas more room and time is given for more specific detailed written feedback. As one knows, feedback can lead to cognitive dissonance, that is, disagreement (Goldring, 2015) so that feedback needs to be given with consideration. To summarize the research intention: The project is done from the perspective of typical university teachers to be able to provide edubot-supported, improved specified feedback on students’ written midterm assignments.

Research question: What kind of support can edubots provide for teachers working on written feedback reports?