Maximum Similarity Index (MSI): A metric to differentiate the performance of novices vs. multiple-experts in serious games

Loh, Christian Sebastian; Sheng, Yunlong

doi:10.1016/j.chb.2014.07.022

Cited by 34 publications

(23 citation statements)

References 29 publications

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“…Learner's understanding is defined as learner's experience in achieving learning objective. Timing the game completion constrain the players' performance since they are forced to decide a solution a hurry and risky [4]. The longer the time completion, the more accurate will the solution be performed by the player [20].…”

Section: Data Type and Value Of Gameplaymentioning

confidence: 99%

“…Conversely, [21,22] state that response time of test item is influenced by intelligence that intellectual ability positively correlates with person speed in processing information. Referring to [4,20,21,22], we restrict the time completion time with a value of 10 minutes (600 sec). It representing a very long completion time and it is never revealed as BoTySeGa gameplay data.…”

Section: Data Type and Value Of Gameplaymentioning

confidence: 99%

“…Serious game provides an opportunity for players to transferring technology and recording player behavior while playing the game [8]. The history of the application of game technologies in education can be tracked at [4,9,10,11,12,13,14] Bloom!s taxonomy theoretically explains that learners! mastery in a cognitive domain was really affected by their mastery on the previous domains [14].…”

Section: Introductionmentioning

confidence: 99%

“…The development of the game technology has attracted researchers to examine the opportunity of its application as an educational assessment tools [3,4]. One such game among others believed as developing technology is a serious game [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Intelligent Classification of Learner's Cognitive Domain using Bayes Net, Naïve Bayes, and J48 Utilizing Bloom's Taxonomy-based Serious Game

Sukajaya

Purnama

Purnomo

2015

Int. J. Emerg. Technol. Learn.

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Abstract-Lately, personalized learning approach attracted the attention of so many researchers due to its capacity to improve the quality of educational system. This approach provides opportunities to maximize the potential of all students based on their profile. This indicates the necessity of grouping learners! ! profiles appropriately in order to optimize contribution of personalized learning approach in achieving learning objective. The problems occurred when classifying learners! ! profile especially when dealing with large number of learners, restricted time to classify, and requirement of authentic data. To solve the problem, we proposed the implementation of Bloom! !s taxonomy-based serious game as an assessment tool replacing paper-based tool for the gameplay data collection. Three different methods namely: BN, NB, and J48 were implemented to obtain the highest accuracy of classification. Our study finds that the NB classifier gives the highest percentage accuracy that is 92.31%. This classifier has the similar accuracy with BN but with lower error rate. In view of the strength of agreement, the result is categorized Very Good (" " = 0.85).

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Section: Data Type and Value Of Gameplaymentioning

confidence: 99%

Section: Data Type and Value Of Gameplaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Classification of Learner's Cognitive Domain using Bayes Net, Naïve Bayes, and J48 Utilizing Bloom's Taxonomy-based Serious Game

Sukajaya

Purnama

Purnomo

2015

Int. J. Emerg. Technol. Learn.

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show abstract

“…It provides an opportunity for players to transferring technology and recording player behavior while playing the game [14]. The history of the application of game technologies in education can be tracked at [15] [22]. But, instructors do not fully embrace serious game in learning.…”

Section: Introductionmentioning

confidence: 99%

A New Approach of Learners' Assessment Using Bloom's Taxonomy-Based Serious Game

Sukajaya

2016

kursor

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AbstrakNaskah ini mendeskripsikan skenario, spesifikasi agen, peta pengetahuan, implementasi serious game berbasis taksonomi Bloom (BoTySeGa), serta tanggapan pengguna terhadap game yang dibangun. Pembangunan BoTySeGa bertujuan menyediakan alternatif alat penilaian yang dibutuhkan dalam penilaian pembelajaran di SD. Pembangunan mempertimbangkan aspek: pengetahuan permainan, materi bangun datar jajaran genjang siswa SD kelas 5, dan domain kognitif menurut Bloom. Level tantangan disusun mengikuti aspek domain kognitif Bloom yang diaplikasikan di jenjang SD (pengetahuan, pemahaman dan aplikasi). Game yang dihasilkan diuji melalui uji penerimaan pengguna (UAT) untuk meyakini semua fungsi dan fitur berfungsi dengan benar. Uji juga mencakup tanggapan pengguna yang dilakukan menggunakan kuesioner skala Likert dengan lima pilihan dan lima belas item pertanyaan. Uji dengan melibatkan 85 pengguna diperoleh hasil bahwa BoTySeGa memenuhi spesifikasi kebutuhan penilaian pembelajaran. Dengan rentangan skor tanggapan 5 -75; rata-rata skor tanggapan dari pengguna sebesar 59,93 dan berada dalam kategori tanggapan "Positif".

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A Learning Analytics Approach to Build Learner Profiles Within the Educational Game OMEGA+

Chandrasekaran¹,

Chang

Graf

2022

Intelligent Tutoring Systems

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Maximum Similarity Index (MSI): A metric to differentiate the performance of novices vs. multiple-experts in serious games

Cited by 34 publications

References 29 publications

Intelligent Classification of Learner's Cognitive Domain using Bayes Net, Naïve Bayes, and J48 Utilizing Bloom's Taxonomy-based Serious Game

Intelligent Classification of Learner's Cognitive Domain using Bayes Net, Naïve Bayes, and J48 Utilizing Bloom's Taxonomy-based Serious Game

A New Approach of Learners' Assessment Using Bloom's Taxonomy-Based Serious Game

A Learning Analytics Approach to Build Learner Profiles Within the Educational Game OMEGA+

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