Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022)

Sghir, Nabila; Adadi, Amina; Lahmer, Mohammed

doi:10.1007/s10639-022-11536-0

Cited by 70 publications

(29 citation statements)

References 90 publications

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“…Furthermore, the high interpretability of the results suggests that the analysis procedure could be employed as a method for interpreting other analytics. Predictive learning analytics, for example, strive to identify at‐risk students within a classroom but do not typically yield easily explainable models (Sghir et al, 2022). Thus, combining information about self‐regulation tactics and strategies employed by students with predictive learning analytics can provide a robust foundation for implementing meaningful interventions to enhance learning and support struggling students.…”

Section: Discussionmentioning

confidence: 99%

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Zhidkikh

Saarela

Kärkkäinen

2023

Computer Assisted Learning

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BackgroundMeasurement of students' self‐regulation skills is an active topic in education research, as effective assessment helps devising support interventions to foster academic achievement. Measures based on event tracing usually require large amounts of data (e.g., MOOCs and large courses), while aptitude measures are often qualitative and need careful interpretation. Precise and interpretable evaluation of self‐regulation skills in a normal K‐12 classroom thus poses a challenge.ObjectivesThe present study proposes and explores a learning analytics method of combining aptitude and event measures to evaluate student's self‐regulation skills.MethodsAn explorative learning analytics study was conducted in a junior high school mathematics class ( students), using a three‐lesson intervention with digital learning materials. Students first assessed their self‐regulation skills with a self‐report questionnaire, after which trace logs and observations of student behaviour were collected. Learning sessions were extracted from trace logs, clustered, and linked to learning strategies. Students were clustered by the self‐report results and learning behaviour profiles. Session clusters, student behaviour clusters and assignment grades were also tested for association.Results and ConclusionsThe detected session and student behaviour types were linked to learning tactics and strategies found in prior studies. Additionally, association was found between self‐reported self‐regulation skills and the student behaviour obtained from trace logs.ImplicationsThe results demonstrate the feasibility of concurrently using aptitude and event measures on a classroom scale, providing teachers with a tool to evaluate and support self‐regulated learning. Combined with further measures like predictive learning analytics, teachers can obtain an early and highly interpretable picture of at‐risk students in their classes.

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Section: Discussionmentioning

confidence: 99%

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Zhidkikh

Saarela

Kärkkäinen

2023

Computer Assisted Learning

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“…While using only these two databases has its limitations, for example introducing bias towards the natural sciences (Mongeon & Paul‐Hus, 2016) (see the Limitations section), it is accepted practice in many meta‐analyses, systematic and scoping reviews (Kumpulainen & Seppänen, 2022; Zhu & Liu, 2020). A number of other systematic reviews in MMLA and LA have also only used Web of Science and Scopus (Amo‐Filva et al, 2023; Crescenzi‐Lanna, 2020; Sghir et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

Multimodal learning analytics—In‐between student privacy and encroachment: A systematic review

Prinsloo,

Slade,

Khalil

2023

Brit J Educational Tech

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Since the emergence of learning analytics (LA) in 2011 as a distinct field of research and practice, multimodal learning analytics (MMLA), shares an interdisciplinary approach to research and practice with LA in its use of technology (eg, low cost sensors, wearable technologies), the use of artificial intelligence (AI) and machine learning (ML), and the provision of automated, mostly real‐time feedback to students and instructors. Much of MMLA takes place in experimental and laboratory settings, researching students' learning in in‐between spaces—between research and classroom application, in‐between students' learning in private and public spaces as researchers track students' learning both in their use of social media and connected devices, and through the use of context‐aware and adaptive devices; and lastly, in‐between respecting students' privacy while increasingly using intrusive technologies. This study seeks to establish what is known about MMLA in terms of rationale for applications, the nature and scope of data collected, the study contexts, evidence of commercial interests and/or downstream uses of students' data, and consideration of ethics, privacy, and the protection of student data. This systematic review analysed 192 articles using a search string consisting of various combinations of multimodal (data) and learning analytics. The main findings include, inter alia, that though MMLA provide insights into learning and teaching, there is little evidence of MMLA findings successfully being applied to classroom settings, at scale. Given that the nature of MMLA research often necessitates the use of a range of (intrusive) sensors and recording technologies and can include children in its samples; the encroachment of students' right to privacy is a huge concern that is not addressed. There is also a need to reconsider the rationale for collecting multimodal data, the conditions under which such data collection will be ethical and in service of students' wellness, and the boundaries that should protect their (multimodal) data. Practitioner notesWhat is already known about this topic Experimental educational research predates both multimodal learning analytics (MMLA) and educational data mining (EDM). MMLA has been an integral part of learning analytics as research focus and practice since its inception. The increased digitisation and datafication, advances in technology (eg, sensor technology, geo‐tracking, etc.) and a growing normalisation of wearable technologies provides greater scope for collecting multimodal data. There is a vast body of published MMLA research providing a range of insight into students' and educators' behaviours aiming to increase the effectiveness of teaching. What this paper adds While there are several studies providing insights into the state of MMLA research, this study provides insight to a selected range of factors in MMLA research such as the type of research (empirical/conceptual); the nature and scope of data collected; the sample populations (pre‐higher education, higher education, etc.); evidence of commercial interests or consideration of downstream uses; issues pertaining to consent, privacy, data protection and ethics; and evidence of how findings were used to improve teaching and learning. The vast majority of MMLA research targets higher education, is empirical in nature and is based on relatively small samples of participation in experimental settings. Confirms previous research that found the predominance of small samples, and a lack of replicability and, as a result, lacking scalability. That there is very little explicit discussion of the ethical and privacy implications and data protection, either at design stage or for future implementation. Similarly, that there is little consideration of potential commercial interests or downstream uses of data. Implications for practice and/or policy MMLA in its essence requires interdisciplinary approaches and teams. For MMLA to move beyond small‐scale, experimental settings to application in real (classroom) settings, larger, replicable studies should be conducted together with ways to make study findings actionable for teachers and students. Ethical issues, commercial interests and downstream uses of collected data must be considered within the design and approval of MMLA research.

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“…Additionally, predictive analytics is an AI domain that can leverage past data to make predictions about future events. In the medical domain, predictive analytics has the potential to anticipate patient outcomes and aid healthcare practitioners in their treatment decision-making [ 47 ]. Furthermore, Natural Language Processing (NLP) is an AI field focused on enabling interaction between humans and computers using natural language.…”

Section: Clinical Practice and Research Perspectivesmentioning

confidence: 99%

The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN’s Challenge

et al. 2023

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Artificial intelligence (AI) is a powerful tool that can assist researchers and clinicians in various settings. However, like any technology, it must be used with caution and awareness as there are numerous potential pitfalls. To provide a creative analogy, we have likened research to the PAC-MAN classic arcade video game. Just as the protagonist of the game is constantly seeking data, researchers are constantly seeking information that must be acquired and managed within the constraints of the research rules. In our analogy, the obstacles that researchers face are represented by “ghosts”, which symbolize major ethical concerns, low-quality data, legal issues, and educational challenges. In short, clinical researchers need to meticulously collect and analyze data from various sources, often navigating through intricate and nuanced challenges to ensure that the data they obtain are both precise and pertinent to their research inquiry. Reflecting on this analogy can foster a deeper comprehension of the significance of employing AI and other powerful technologies with heightened awareness and attentiveness.

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Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022)

Cited by 70 publications

References 90 publications

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Multimodal learning analytics—In‐between student privacy and encroachment: A systematic review

The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN’s Challenge

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