HousingHK Simulation Game on Soqqle

This article is only for internal use/review only, and will be taken down by July 31st or a publication approval date, whichever is earlier.

As the educational paradigm evolves, greater emphasis will be placed on critical thinking and problem solving skills, which require adoption of non-traditional pedagogical approaches such as gamification (Arocena & Sutz, 2021; Luna-Nemecio et al., 2020). Although some view computer games as a mere distraction from formal learning, a growing body of evidence shows that they can improve motivation, engagement, and academic achievement (Manzano-León et al., 2021), thus enhancing learning outcomes (Tsai et al., 2020; Yildirim, 2017). Through games, users learn to overcome challenges and compete and/or collaborate with other participants when solving elaborately designed simulated problems. These benefits of gamification, in particular simulations, should therefore be considered when designing academic curricula (Sierra, 2020). As simulations aimed at education can enhance knowledge acquisition, problem-solving, and critical thinking skills, there is a need to study digital forms of assessment that focus on their learning outcomes (Farrell, 2020; Hallinger & Wang, 2020). Thus, the goal of this study is to determine if students find a digital simulation of the Hong Kong housing market beneficial for gaining knowledge about its dynamic and property purchasing process, and whether they consider the adopted assessment criteria authentic and meaningful.

2 Literature Review

Housing market in Hong Kong is complex, as the island’s limited geographic area has created an imbalance between supply and demand (Zheng et al., 2018), leading to unaffordability (Ma et al., 2018; Ho et al., 2019; Leung et al., 2020). Consequently, most youths cannot purchase a home in the city, leading to distrust and dissatisfaction with the ‘housing system’ (Forrest & Xian, 2017). Luna-Nemecio et al., 2020 is of the opinion that improving such a situation starts with better understanding of problems and employing more critical and complex thinking, which are particularly crucial when innovative solutions are required (Arocena & Sutz, 2021; Falloon, 2019).

As the future employment and life prospects (Haug & Mork, 2021) of youth also depend on these 21st century skills, Chan and Yuen (2014) postulated that more innovative teaching methods must be adopted at all levels of education. Concurring with this view, Parker and du Plooy (2021) used a team-based game to encourage students to embrace making mistakes in a psychologically safe environment, which led to improved idea sharing and problem solving. However, Roberts et al. (2010) argued that such free, open, and honest exchanges can only occur in an appropriate social and technical setting.

Learning environments based on experiential learning and simulations aimed at connecting concepts taught in the classroom to real life are becoming increasingly popular (Fry & Kolb, 1979; Qian & Clark, 2016; Farashahi & Tajeddin, 2018; Hallinger & Wang, 2020). However, experiential learning is not a new concept, as more than four decades ago Fry and Kolb (1979) claimed that it helps students gain multiple perspectives and consider diverse solutions to gain new experiences, which is particularly beneficial to liberal arts education. This learning mode can be supported by simulations, which aid with organizing, conceptualizing, and applying knowledge to encourage active learning. Simulations are also often designed with technical enhancements like automated learning analytics (Ifenthaler et al., 2017) that improves teaching efficiency. These benefits are aptly demonstrated by García-Magariño & Lacuesta (2017) who designed an online real estate simulation that incorporates economic objectives, spatial information, negotiations, property characteristics, and real-estate agent roles, among other features. The authors argued that, by allowing students to act as buyers and sellers, they would be motivated to analyze simulated real estate transactions thus improving their knowledge on the real-life housing market dynamics. In an earlier study, Zhang et al. (2014) also designed a simulation model consisting of demand generation (housing attributes and utility), information search (supply/demand fundamentals, market environment), and price negotiation (supply/demand pricing, transaction completion), aiming to increase participants’ understanding of these concepts.

While these investigations have offered some valuable findings, as they focus on one-off learning opportunities (Sierra, 2020; Mochizuki et al, 2021), they offer limited information on the ways of assessing learning outcomes in education-focused simulations that would be implemented for longer periods (Ifenthaler et al., 2017; Hallinger & Wang, 2020). For example, playing a game during class periods with extended gaps between consecutive sessions can disrupt the learner’s experience (Moizer et al., 2019), especially where modern media like images or videos are not involved. Therefore, allowing students to take part in an online simulation game at their own pace might boost their engagement (van Roy & Zaman, 2018), but will require adoption of carefully planned assessment modes.  

In this context, authentic assessments (Martin et al., 2019, Sokhanvar et al., 2021) can be particularly valuable, as they are based on real-world tasks that support experiential learning (Vos, 2015), and increase student engagement and satisfaction (Sokhanvar et al., 2021), while relating gained knowledge to real-world applications (Farrell, 2020). For this study, we drew upon six attributes of authentic assessments, namely knowledge transfer, reflection, performance, complex and challenging, real-world applicability, and varied experiences (Farrell, 2020). Specifically, in the context of the present investigation, knowledge transfer refers to the ability to apply the knowledge gained from the simulation to the Hong Kong housing market, or actual real estate transactions.

Further, it is also useful to investigate the right level of fidelity (extent of realism, to which the simulation accurately matches real-life) necessary for the simulation (Alexander et al., 2005; Hindmarsh et al., 2014). Whilst most of the simulations discussed earlier take place in high-fidelity environments, a lower-level fidelity model might increase educator and student adoption (de Bruijn & Leeman, 2011; Kleinheksel, 2017). Given social media’s popularity, using such a familiar format might remove the need for special user training and therefore reduce the setup time and effort. Besides technological and implementation factors, it is also important to consider the psychological effects of the simulation in comparison to real life (Kozlowski & DeShon, 2004), otherwise known as psychological fidelity.  The authors argue that a simulation can be low-fidelity in realism and yet achieve high levels of psychological fidelity if effective instructional strategies are incorporated into the game design.

Recent developments in game simulations (Argasiński & Wȩgrzyn, 2018; Mochizuki et al., 2021), focusing on the following core principles can be provide guidance for designing simulations (Figure 1):

● Game design that allows users to adopt different and sometimes opposing roles and employ different resources, while interacting (or storytelling) with each other and making decisions that affect other participants

● Game sessions that describe the actual experiences of players (e.g., interface, quests) that induce emotions and promote decisions based on systematic game mechanics driven by rules (e.g., consequences, clues, relations, references)

● Game outcome that describes potential task and/or learning objectives in accordance with the different roles players adopt

● Challenges that players with different roles need to overcome which motivate better performance

● Social learning is facilitated by interactions between players, either as collaborators or competitors, designed to be openly accessible by select players in the game to support knowledge co-creation.

Figure 1 The HousingHK game design based on Mochizuki et al.’s (2021) model

Given the aforementioned game objectives, the following research questions guided the present investigation:

1. Do students perceive gamification in a low-fidelity social-media simulated environment as beneficial to their learning outcomes (better understanding of the Hong Kong housing market)?

2. Do students perceive the simulation’s assessment attributes as meaningful and authentic? 

3 Method

3.1 Participants

The study sample consisted of 23 undergraduate students enrolled in the “Hong Kong Housing Market” module as a part of a Bachelor’s degree at Liberal Arts University in Hong Kong. Most of the study participants resided in Hong Kong during the study.

3.2 Materials

The simulation game was facilitated in a purpose-built social learning mobile application, Soqqle (https://soqqle.com), used in multiple institutions in Asia, where users can “post” images or videos as well as add personal comments on these submissions. Prior to the simulation game, a code-name was created on the Soqqle application and given to the students through email. Students subsequently downloaded the Soqqle application on the Android playstore or IOS appstore and entered the code-name into the application to join the game. The code-based protection provides a private and safe environment where students can share their ideas and engage with others anonymously.

3.3 Procedure

For three consecutive weeks in March 2021, participants were instructed to join a real estate simulation game.

3.3.1 Game Setup. Within each round, buyer profiles (consisting of their income, savings, and preferences) are created (see Appendix) and shared with sellers, who work as a team of 3−4 students (to simulate a real-estate agency) to identify suitable properties that would be “marketed” to buyers. Buyers are also informed of their profiles through email and are instructed to start searching for properties.

3.3.2 Game Process. Sellers start by searching for homes on public property websites like Centraline Property (https://hk.centanet.com) or Midland Property (https://www.midland.com.hk). Once they identify suitable homes for sale, they uploaded all relevant details on Soqqle , making this information accessible to all players. This generates a push notification on Soqqle (similar to a social media application), indicating that a new property has become available. After reviewing all advertised homes, buyers can decide whether to bid on any of the postings depending on their budget (e.g., savings, down payment requirements) and preferences (e.g., property size, location, proximity to transport links, etc.). If they decide to make a bid, they would post a bid amount on Soqqle and wait for the seller to respond.

3.3.3 Game Completion. After receiving and evaluating all bids, sellers will reply to indicate acceptance or rejection. If the transaction is completed, the relevant property will be marked as SOLD, informing other buyers to exclude the sold property from their search.

The objective of this game design is to allow participants to experience a simulation of an actual property search process, which involves weighing their preferences against their financial capacity and market trends. The overall design is shown in Figure 2. 

Figure 2 Detailed HousingHK game design informed by Mochizuki et al.’s (2021) model

3.4 Data Collection and Analysis

To determine if participants found the simulation game beneficial for learning the specifics of the Hong Kong housing market, they were asked to write a 500-word reflective essay, in English, about the HousingHK game at the end of the semester, discussing the lessons learned from their experience. These essays were subjected to content analysis to determine if the simulation achieved the predefined learning objectives (understanding the housing search process, housing attributes, and housing market in Hong Kong).

To establish if students found the assessments authentic and meaningful, an independent rater classified keywords from the essays into authentic assessment categories proposed by Farrell (2020), as shown in Table 1. For example, keywords that match housing attributes or pricing calculations were categorized under “knowledge transfer,” while keywords indicating self-reflection on the housing market were classified under “reflection.” Once all individual essays had been analyzed, all references related to a particular attribute were counted to determine the overall student experience.

Table 1 Six authentic assessment attributes for HousingHK based on Farrell (2020)

When coding essays and analyzing data, student names were removed to ensure participants’ anonymity. Moreover, participants were informed that if they preferred not to have quotes from their essays included in any reports or publications, they could notify the researchers of this decision by email.

4 Results

4.1 Perception of Simulation’s Utility for Gaining Knowledge about Housing

The 23 participants (6 teams) posted 35 properties on the Soqqle mobile app during the study period, 21 (60%) of which were sold. When submitting new postings, sellers included images, as well as relevant attributes such as price, location, number of rooms, age of the building, floor at which the advertised property is located, proximity to transport links, while buyers placed bids using comments, as illustrated in Figure 3. Only 21 participants submitted their essays at the end of the simulation, which were coded by an independent rater who mapped identified keywords into the authentic assessment categories.

Figure 3 Examples of posts, bidding, and feedback on Soqqle application

Analysis of the 21 essays revealed that, for most participants, playing roles of both sellers and buyers was a meaningful experience, whereby they found pricing calculations using the online calculators, successful purchases through the bidding process on Soqqle, or mistakes made when homes were not sold successfully, particularly beneficial.

Illustrative quotes extracted from student essays demonstrating their attainment of learning outcomes

Housing search process

“. . . given the assigned attributes of 25 buyers, including their budget constraints (monthly household income & saving) and preferences (location, number of rooms, building age, floor level, transportation & liquidity). . . . we first chose houses from Kowloon because there are 10 buyers who want houses from that area, excluding those who are indifferent between location choices.”

“The property price of Hong Kong Island is generally more expensive due to the high demand. I searched the properties with 3 rooms on Hong Kong Island on the internet. Most of them are more than 8M. In the contrast, I can find many properties with 3 rooms in New Territories which are around 6M to 7M. . . .”

Pricing based on housing attributes

“Eventually, I successfully bought a 3-room flat in Shau Kei Wan which is worth $7.38 million . . . I still need to spend 14 years to finish the mortgage repayment of this house. Having 1 million of savings, I would spend $738K to settle the down payment, which is 10% of the total current price of this house."

“I decided to do maximum of 88% LTV with a monthly repayment amount of 19K, the down payment of 74K and the base income requirement is 44K. For the above calculation, I have used the interest rate of 1.4% according to the latest bank policy. “

Overall housing market

“According to the survey completed by the government, the mean monthly income per person was  HKD 19K and only about 38% of the population earned 45K or higher, so it showed that it is extremely difficult for a normal people to afford an apartment.”

“Last but not least, I had some new perception towards the housing market after the economic game. . . .We save most of our income to pay for the down payment, even though we can afford the down payment, we still have to pay for the housing mortgage for the rest of our lives.”

4.2 Perception of Authentic Assessments

Based on an independent rater’s mapping of keywords identified in students’ essays into the authentic assessment categories (Figure 4), knowledge transfer was mentioned most frequently (with an average of 11 keywords per student) followed by reflection (with an average of 3 keywords each), both of which featured in every essay. Additionally, 55% of students used the keywords that could be assigned to the “complex” and “challenging” categories, while 36% and 32% mentioned terms that could relate to “real world applications” and “varied activities,” respectively. Overall, more than 50% of keywords per student were assigned to four of the six authentic assessment attributes.

It is also worth noting that, although not every student mentioned “varied activities,” the roles (buyer or seller) they played were cited in most essays. Likewise, although not all students mentioned keywords related to real world applicability (e.g., realistic, real), this could be inferred from references to student experiences of the simulation featuring in most essays.

Figure 4 : Student responses by authentic assessment categories

Authentic assessment categories and illustrative quotes for HousingHK

  • Complex and Challenging, keywords: difficult, problems
    “Consider a lot of factors before purchasing a property.”
    “People need to decide which one is the priority and sacrifice some factors.”
  • Knowledge Transfer, keywords: liquidity, payment, households, repayment, income
    "My monthly income does not allow me to afford a monthly mortgage payment for a house which fully matches my preferences”“By inputting the mentioned data to the online mortgage calculator, . . . I was able to loan HKD 4,944,817 and afford a monthly repayment, HKD 18,650.15.”
  • Real World Applicability, keywords: real, realistic
    “Reality in Hong Kong.”
    “I think this game is more realistic.”
    “The game reflects the reality and makes me have a deeper understanding regarding the housing market.”
  • Reflection, keywords: understand, meaningful, useful, insight, strong
    “Stronger motivation for having a higher salary . . . in order to pass the stress test.”
    “Extremely difficult for a normal people to afford an apartment.”
    “As a seller in the game, I think our group can do better next time.”
  • Varied Experiences, keywords: seller, buyer
    “Simulate the process of purchasing properties on both the buy-side and the sell-side.”
  • Performance, keywords: successfully, bought, sold, purchased
    “In the final result, we can only sell 3 of the 6 housings we posted, which I think is not the best result.”
    “I am glad that I could manage to match all the attributes of the unit with my preferences.”
    “The house information I posted on the Soqqle app . . . is suitable for Buyer No. 9 and Buyer No. 13 . . . Although they did not buy my house in the end, I have tried my best.”

5 Discussion

The findings yielded by this study provide insights into the drawbacks and benefits of conducting a simulation game as a means of teaching liberal arts education students about the Hong Kong housing market. As most students achieved the learning goals by partaking in the complex-thinking driven simulation as buyers and/or sellers of properties in Hong Kong, they felt that this experience was beneficial (RQ1). Similarly, in their essays, students consistently used expressions that corresponded to authentic assessment attributes, suggesting that authentic assessments might be a suitable framework for measuring the learning outcomes of HousingHK game users (RQ2).

Many students that took part in this study described the game as realistic and meaningful, as the simulation appeared to accurately reflect the state of the housing market in Hong Kong. Many also noted that their knowledge regarding the requirements that need to be met to purchase a property was significantly improved by taking part in the game. It is also worth noting that, even though more than half of the participants encountered challenges, they still managed to complete the tasks, indicating that optimal challenges in simulation games promote learning (Buil et al., 2019; Kiili, 2005). These reflections suggest that simulations are suitable for imparting knowledge on non-linear subjects (such as housing valuation) and can yield successful learning outcomes (Lainema & Nurmi, 2006; Falloon, 2019).

The HousingHK game can be classified as one of lower-level fidelity (36% mentioned realism) as it does not replicate day to day market forces and decision-making that a high-level fidelity might. Empirical evidence (Nicolaides et al., 2020) indicates that whilst both types can yield performance improvements, the level of fidelity used should reflect factors like level of participants, type of tasks and resources required. Particularly, game resources (computer software or physical equipment) should be easy to acquire and use (Kozlowski & DeShon, 2004; de Bruijn & Leeman, 2011). We purport that the HousingHK simulation’s low-fidelity design (in comparison to a real-world environment) was effective given the quick and simple setup of the game environment and how easy the students got started to complete the tasks. A higher-level fidelity might have otherwise impacted student participation and motivation due to psychological stress (Alexander et al., 2005).

Given these encouraging findings to acquire complex thinking skills for learning the housing market in Hong Kong, HousingHK could be enhanced further by improving its design, thus increasing its educational potential. While in this initial version, we leveraged some core game design elements proposed by Mochizuki et al. (2021), the app’s value for social learning could be optimized to enhance collaboration and peer-to-peer interaction. This could be achieved by requiring participants to upload self-reflection videos at the end of each round, allowing other players to comment on their content to build knowledge and promote discourse (Teo, 2019). As noted by Farrell (2020), collaboration and feedback are key authentic assessment attributes, but were not in focus of this study. Thus, in future research, HousingHK could be expanded by adding other social learning activities to increase assessment effectiveness. In addition, statistics can be used to derive correlations between user activities (e.g., number of housing views, number of mortgage calculation attempts) and learning outcomes (based on the essays) to identify factors that are most influential in this process. Finally, whilst the authors believe that the private nature of Soqqle facilitates an environment that promotes psychological safety, it would be beneficial to evaluate the personal and interpersonal factors that encourage participation through additional investigations.

6 Conclusion

The present study aims to enhance our pedagogical understanding of teaching complex thinking skills such as the Hong Kong housing market using low-fidelity gamification and simulation. It is also the first simulation performed in a private social media style environment that consistently met student learning outcomes through recent studies in game design (Argasiński & Wȩgrzyn, 2018; Mochizuki et al., 2021) and integration of authentic assessments (Farrell, 2020). Future research is recommended to strengthen the understanding of the depth of learning outcomes as well as potential correlations with the different aspects of game design. The findings yielded by the present study can be of value to educators striving to incorporate new educational methods, such as simulations, into their curricula to encourage students to develop complex problem solving and critical thinking skills. Therefore, factors involved in integrating online simulation games to curricula and assessments require more in-depth investigation.

7 Limitations

This study aimed to establish if authentic assessments are suitable for determining student outcomes based on participation in the HousingHK game. However, the depth of learning outcomes achieved by each student was not evaluated. Therefore, the study can be improved further by identifying specific aspects of the game that played the most significant role in participants’ ability to attain learning outcomes, as well as by measuring student motivation and participation. Nonetheless, the results reported in this work demonstrate that our game design framework can be used for understanding the Hong Kong housing market dynamics.

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Daniel Shen

Daniel Shen