- Nov 2023
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hongs1013.github.io hongs1013.github.io
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In-game tutorials explicitly scaffold the designed pathway for players inLakeland, nudging playerstowards self-sustaining strategies. However, the game grants achievements to players in fourcategories, which may engage different types of players. For example, some players may focus ongaining achievements for a large population, many farms, or money earned. On the other hand,some players may follow a path of destruction, progressing primarily along the algae bloom track.This tension between the designed (and instructed) pathway and the varied achievements leads toopportunities for divergent play. In this case study, the objective is to use our method to evaluate thegame’s existing design, by examining the players’ decisions and progress, and how they interact withgame feedback.
these are things i noticed while playing the game
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A Method for Clustering Styles of Gamepl
these methods are a little different than the last papers methods for gameplay learning
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through qualitative approaches such as direct observation,think-alouds and structured interviews.
so there isn't data sources and quantitative data this in kind of like a sociology based project where you are asking for the pieces of how the games make people feel or think
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The ability for an educational game designerto understand their audience’s play styles andresulting experience is an essential tool for improving their game’s design. As a game is subjected tolarge-scale player testing, the designers require inexpensive, automated methods for categorizingpatterns of player-game interactions.
So this paper isn't as interested about the educational process instead more about the player interactions and categorization
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hongs1013.github.io hongs1013.github.io
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There was a negative association between age and academic attainment. Younger studentswere more likely to complete a course. Students with higher prior educational qualifications hada higher chance of completing, passing and achieving excellent grades.
Interesting I wonder why this is
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ncon-scious bias in the nature of feedback that 470 BME students and 470 matched White studentsreceived on their assignment from their tutors.
This idea right here with unconscious bias in the feedback is so important
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That means the attainment gap can be explained by differences in studentprior qualifications, with more BME students having a lower prior qualification than White stu-dents when they entered universities
this is also true for the US and colleges that allow students to test out of classes, or students do better become from a better academic background and environment.
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white categorisation should be critically debated, as the term ethnicity is socially constructed,and people may have multiple identities.
this is a wonderful point as those deemed white in the Uk I wonder are actually white or white presenting with different ethnics backgrounds. Especially since this is based on self identification.
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The latest review by Richardson(2018) which synthesised data from multiple sources over the last 20years showed that the odds ofobtaining a good degree (i.e. first-class or upper second-class) in BME students are about half thosein White students. This under-attainment effect was stronger for Black students compared to Asianstudents.
Well its insane that we have so much data about these disparities that we talk about so much in american but turns out that this is common in the UK and possible in all colonized setting across the globe where institutionalized systemic racism in education may be creating these disparities.
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nterdisciplinary field of learning analytics hasdemonstrated its potential to identify students who may need additional support from an earlystage and provide real-time interventions (
something that being interest to me for my project is see the benefits and the disadvantages of such systems for students of color and those of high mental anxiety populations
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- Sep 2023
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learning-analytics.info learning-analytics.info
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discrimination-free based simply on the fact that the algorithms do not use discriminationcategories.
yes!
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This means that even if training is done with a quantifiable goal criterion and no protected category variables, algorithms canstillbe quite discriminatory if a “proxy”variable is included that correlates with both the quantifiable goal criterion and one or more protected category variables.
its so important to understand there is unconsious bias in many of these algorithms due to the way things are set up structurally within the code of system.
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www.solaresearch.org www.solaresearch.org
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This direct involvement of student voices inshaping a policy dealing with the ethics of learninganalytics offered unique insight into the ways in whichstudents regard their data — as a valuable entity to becarefully protected and even more carefully applied
If you include the people the research is being done on there is less chance that ethical slips will happen. awareness and consent always make for a better project.
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An increasing awareness of learning analytics as ameans of doing something to the student withoutthat student necessarily knowing triggered furtherH[SORUDWLRQRILVVXHVDURXQGVXUYHLOODQFHVWXGH
this is a great point if I do not know really what’s going on with the data that is being collected on me and maybe I am never told after that data is collected I am going to be suspicious about what is done with that information
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HEIs (higher education institutions) to proactivelyidentify and support students at risk of failing or drop-SLQJRXWWKH\GRVRLQDFRQWH[WZKHUHE\UHVRXUFHVare (increasingly) limited.
this would be a great place to use learning analytics ! Ensuring we are seeing patterns where students are not doing well and changing these patterns to perpetuate student success.
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PLQFUHDVHGGDWDharvesting, we should not ignore the possibilities ofÜGDWDSUR[\LQGXFHGKDUGVKLSâZKHQWKHGHWDLORE-WDLQHGIURPWKHGDWDSUR[\FRPHVWRGLVDGYDQWDJHLWVHPERGLHGUHIHUHQWLQVRPHZD\Ý 6PLWK
how can we combat data harvesting? This seems to be an intrinsic problem of data and giving so much information to website.
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Learning analytics as moral practice — focusingnot only on what is effective, but on what is ap-propriate and morally necessary2. Students as agents — to be engaged as collabora-tors and not as mere recipients of interventionsand services3. Student identity and performance as temporalG\QDPLFFRQVWUXFWVØUHFRJQL]LQJWKDWDQDO\WLFVprovides a snapshot view of a learner at a particularWLPHDQGFRQWH[W4. 6WXGHQWVXFFHVVDVDFRPSOH[PXOWLGLPHQVLRQDOphenomenon5. Transparency as important — regarding the pur-poses for which data will be used, under whatconditions, access to data, and the protection ofan individual’s identity That higher education cannot afford not to use dat
I really like the second point to make sure to keep students engaged as collaborates and not just as the people getting data collected on themselves. It’s also very true that when we are working on and doing analytics that we see this is just a snapshot in time of the data and the students the data are being collected from. We also want to make sure we can be transparent and open about what we are collecting and why this even needs to be collated. Finally we need to collect this data for some reasons like the ones we talked about which are accreditation, teacher feedback and checking in on student learning.
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increasing surveillance and the (un)warranted col-OHFWLRQDQDO\VLVDQGXVHRISHUVRQDOGDWDÜIHDUVDQ
data mining
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