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We Finally Got There in the End

Looking Back at my DA


Ideation & Iteration

Originally the plan for my DA was to stream a different mobile rhythm game every few weeks and in the final project compare the various games with the framework of User Interface, monetisation and storyline/paratexts. I quickly decided against this model, as I wanted to offer a more in-depth analysis and thought focusing on one game, Rhythm Hive, would allow me to do this.

I also wanted to focus on Rhythm Hive because I’d put money into the game – purchasing the monthly subscriptions. I had only intended on paying for one but lost track of the free subscription on the other and ended up paying for both.

Timeline of weeks 10-13
DA Timeline week 10-13

I had wanted to make more content across varying platforms, especially Twitter and YouTube but found that to be too much – it took me longer than expected to set up my stream aesthetic and get to the point where the stream could run seamlessly. Plus, I was picking up more shifts at work, so creating content on top of streaming was out of the picture.

I tried to work with the principles of FIST and be quick to change elements of my project that weren’t working, although putting money into the game wasn’t necessary it is something I often do, and I worked to not put any money into my streaming setup – my logic was that I would put money into streaming if I could make money from it.

One thing that impacted the amount of content produced for my project was motivation. I found myself getting over the game, especially since there weren’t many events offered for TXT (the group I play with) at the time. I’m hoping I was able to overcome this by talking about player engagement in my final DA and using my own experience as an example.

Twitch Channel after I lost all of my streaming data

I also had a major issue with my project as I managed to lose all my streaming data. While I intended to make highlights of my streams, I ended up forgetting that Twitch deletes all stream content after 2 weeks and didn’t realise my own backups weren’t saving properly. This meant that I spend the last few weeks streaming to make up for lost content and making sure to produce highlights that will last.

I worked to iterate based on peer and tutor feedback, and in particular kept an eye on balancing the different elements of my video to make everything cohesive. I didn’t have too much feedback on my beta as I had a very structured plan to make-up for my lost content.


Analytical framework

My analytical framework changed almost entirely from my initial plans, with monetisation being the only concept I held on to. Instead, I chose to focus on player engagement and game customisation as well. While I lost my streams, I’d been keeping an in-depth field journal because I’m using this project for BCM241 as well. I was able to use these notes to support my analysis, as I let my framework influence how I viewed and interacted with the game.

Field notes

I had a few different periods throughout the semester where I’d focus on research, and at the end of this post there is a detailed reference list of all of the sources I used in my DA – I used basically everything I’d found in my background research to support my framework.

Twitch Rhythm Hive videos


The original plan for my project was to make a welcoming space through my streaming, where people could relax and chat. I tried to keep this up but didn’t have many regular viewers – the one regular viewer I did have mentioned that they liked running my stream as they played the game.

 I think my project offers more utility in the fact that I am one of the very few people streaming both Rhythm Hive and SuperStar Ateez – another K-pop based game that I used to break up the monotony. I’ve made an array of stream highlights that people will still be able to view.

Twitch SuperStar Ateez Videos

 I worked to make my final DA accessible to my audience, and think it is unique as there aren’t any other in-depth analyses of the game. I also wanted to talk about my own personal experiences and used the video to compare the different monetisation options and whether they were worth putting money into.

Project Appraisal

Overall, while my project dipped off a bit in the middle, I was able to recover it in the last few weeks. I still struggled with posting on Twitter as there isn’t a major online hub for the game. My project was also limited by the game I choose to focus on, as I couldn’t explore a fan culture that is mostly talked about among friends, and only popular online during updates.

 I think choosing to focus on Rhythm Hive both helped and hindered me. It allowed me to create a more in-depth analysis of the game but meant that I lost motivation to play – something I was originally hoping to overcome by playing multiple games. In the future if I were to do something similar, I’d be much more aware of how long Twitch saves video content, and as suggested by a peer upload the content to another platform as a backup.

 Regardless, I am happy with the level of detail I was able to achieve in my final project, and hope that it will be able to help others.


Abbasi, AZ, Rehman, U, Hussain, A, Ting, DH & Islam, JU. 2021. The impact of advertising value of in-game pop-up ads in online gaming on gamers ‘inspiration: An empirical investigation’, Telematics and informatics, vol. 62.
beamable. 2020. Inspiring Examples of Daily Login Rewards for your Mobile Game. [online] Available at: <> [Accessed 4 October 2021].

Fang, B, Zheng, Z (Eric), Ye, Q & Goes. 2019. Social Influence and Monetization of Freemium Social Games, Journal of management information systems, vol. 36, no. 3, pp. 730–754.

Grosso, W., 2016. The Science & Craft of Designing Daily Rewards -- and Why FTP Games Need them. [online] Game Developer. Available at: <> [Accessed 4 October 2021].

Johnson, MR & Brock, T. 2020, The “gambling turn” in digital game monetization, Journal of gaming & virtual worlds, vol. 12, no. 2, pp. 145–163.

Raessens, J. 2005. Computer games as participatory media culture. In J. Rassens & J. Goldstein (Eds.), Handbook of computer game studies (pp. 378-388). Cambridge, MA: MIT Press

Venturelli, M., 2016. Difficulty Levels And Why You Should Never Use Them. [online] Game Developer. Available at: <> [Accessed 8 October 2021].

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