Nanogenetic learning analytics

Martin, Taylor; Aghababyan, Ani; Pfaffman, Jay; Olsen, Jenna; Baker, Stephanie N.; Janisiewicz, Philip; Phillips, Rachel S.; Smith, Carmen Petrick

doi:10.1145/2460296.2460328

Cited by 13 publications

(7 citation statements)

References 10 publications

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“…Tactics that lead to success can also be discovered with cluster analysis (Sharples and Domingue 2016).  Importance of understanding learners' characteristics: students with different learning characteristics may exhibit different learning behaviors (Liu et al 2016), for instance, different exploration strategies (Martin et al 2013) or, in some cases, their age and gender (Wallner and Kriglstein 2015). It is key to model students for effective adaptive instruction (Koedinger, McLaughlin, and Stamper 2012), for instance, self-regulated learners tend to make better use of in-game curricular resources and may be more deliberate in their actions (Sabourin et al 2013) and high-performance students tend to use tools more appropriately (Liu et al 2015).…”

Section: Results On Assessment and Student Profilingmentioning

confidence: 99%

Applications of data science to game learning analytics data: A systematic literature review

Alonso‐Fernández

Calvo-Morata

Freire

et al. 2019

Computers & Education

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Data science techniques, nowadays widespread across all fields, can also be applied to the wealth of information derived from student interactions with serious games. Use of data science techniques can greatly improve the evaluation of games, and allow both teachers and institutions to make evidence-based decisions. This can increase both teacher and institutional confidence regarding the use of serious games in formal education, greatly raising their attractiveness. This paper presents a systematic literature review on how authors have applied data science techniques on game analytics data and learning analytics data from serious games to determine: (1) the purposes for which data science has been applied to game learning analytics data, (2) which algorithms or analysis techniques are commonly used, (3) which stakeholders have been chosen to benefit from this information and (4) which results and conclusions have been drawn from these applications. Based on the categories established after the mapping and the findings of the review, we discuss the limitations of the studies analyzed and propose recommendations for future research in this field.

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Section: Results On Assessment and Student Profilingmentioning

confidence: 99%

Applications of data science to game learning analytics data: A systematic literature review

Alonso‐Fernández

Calvo-Morata

Freire

et al. 2019

Computers & Education

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“…As for the other observed phenomena, the analysis and application of Learning Processes was present in the research by Martin, T. et al (2013) and Peddycord-Liu, Z. et al (2017), while the research by Callaghan, MJ. et al (2014), Vahldick, A. et al (2017) and Flores R.L.…”

Section: Q1: What Educational Phenomena Does the Research Intend To Amentioning

confidence: 99%

“…The Cluster Analysis technique was present in 5 of the 14 studies, which is equivalent to about 36% of them, being used mainly to analyze the Learning Processes [Martin, T. et al, 2013;Peddycord-Liu, Z. et al, 2017] and Student Performance [Feng, X. & Yamada, M., 2019;Niemelä, M. et al, 2020], also being present in a study that sought to analyze the Problem Solving [Vahdat, M. et al, 2016].…”

Section: Q2: What Techniques / Algorithms Are Used To Analyze the Datmentioning

confidence: 99%

“…Research by Martin, T. et al (2013) and Peddycord-Liu, Z. et al (2017) aimed to observe the same educational phenomenon, use similar techniques, both games are in the area of mathematics and the participants also have the same education, but the number of students analyzed in the first is 21, while in the second survey it is 1565. On the other hand, two of the three surveys whose participants are from higher education "College Students" do not even make clear in their texts the amount of the sample analyzed [Callaghan, MJ.…”

Section: Q3mentioning

confidence: 99%

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Discovery of Research Gaps in Game Learning Analytics Applications on Learning Processes

Silva¹,

Rodrigues²,

Filho³

et al. 2020

Anais Do XXXI Simpósio Brasileiro De Informática Na Educação (SBIE 2020)

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The benefits of game applications in the field of education have been increasingly evident due to the use of Game Learning Analytics, which is the collection, analysis and extraction of information from data obtained through Serious Games. In this article, this article aims to carry out a systematic mapping in order to identify research gaps in studies that use Game Learning Analytics to analyze an educational process, analyzing the observed educational phenomenon, the algorithms or techniques used and the characteristics and number of participants. We understand that even though the Game Learning Analytics area is still very new, it has enormous potential in helping what we value most, teaching.

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“…The first works of Learning Analytics focused on the traces that were automatically generated when learners interacted with some type of digital learning tool. For example, Kizilcec, Piech, and Schneider [21] used the log of the actions performed by different groups of students in massive open online courses (MOOCs) to study course engagement, or Martin et al [26] that use the low-level actions of students playing an educational video game study learning strategies. While these tools fulfill the goal of Learning Analytics, if we only focus on a single type of traces that are recorded in logs of digital tools, we risk oversimplifying the process of learning or even worse, misunderstanding the traces due to the lack of contextual information, two of the main critiques directed towards Learning Analytics from the educational research community [36].…”

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confidence: 99%

Multimodal Learning Analytics: Rationale, Process, Examples, and Direction

Ochôa¹

2022

The Handbook of Learning Analytics

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This chapter is an introduction to the use of multiple modalities of learning trace data to better understand and feedback learning processes that occur both in digital and face-to-face contexts. First, it will explain the rationale behind the emergence of this type of study, followed by a brief explanation of what Multimodal Learning Analytics (MmLA) is based on current conceptual understandings and current state-of-the-art implementations. The majority of this chapter is dedicated to describing the general process of MmLA from the mapping of learning constructs to low-level multimodal learning traces to the reciprocal implementation of multimedia recording, multimodal feature extraction, analysis, and fusion to detect behavioral markers and estimate the studied constructs. This process is illustrated by the detailed dissection of a real-world example. This chapter concludes with a discussion of the current challenges facing the field and the directions in which the field is moving to address them.

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Nanogenetic learning analytics

Cited by 13 publications

References 10 publications

Applications of data science to game learning analytics data: A systematic literature review

Applications of data science to game learning analytics data: A systematic literature review

Discovery of Research Gaps in Game Learning Analytics Applications on Learning Processes

Multimodal Learning Analytics: Rationale, Process, Examples, and Direction

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