Utilizing student activity patterns to predict performance

Casey, Kevin; Azcona, David

doi:10.1186/s41239-017-0044-3

Cited by 59 publications

(34 citation statements)

References 18 publications

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“…Based on the mentioned research, there are activities done by students in working on the problem, whether it is in the form of group work or independent assignments. It is similar to the findings of research conducted by (Casey & Azcona, 2017;Novianti, Ertikanto, & Wahyudi, 2014;Nurmala, 2014;Purwasih, Hendriana, Trawan, Prasetio, & Trisatria, 2018;Yang, Baker, Studer, Heffernan, & Lan, 2019), which discover that in the learning activeness, there is a process of information exchange and changes in students' behavior shown on their activeness. The study by (Purwasih et al, 2018) on students' responses in solving math problems based on the solo taxonomy explains the teaching and learning process gradually paying attention to the level of responses based on the solo taxonomy of the students' skills.…”

Section: Introductionsupporting

confidence: 86%

An Identification of Students’ Responses Based on Solo Taxonomy in Mathematics Learning Toward Learning Activities and Learning Outcomes

Kaharuddin

Hajeniati

2020

ajpm

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Taxonomy solo is a classification of real responses from students. This research aims to identify the effect of students' responses based on a solo taxonomy in mathematics learning on learning activity and learning outcomes. This research is a mixed-method with an explanatory sequential design. The data were collected using observation instruments, questionnaires, interviews, and tests. The data was analyzed inferentially and narratively. Based on the results, students who are at the extended abstract response level are classified as very active and having very high learning outcomes, students who are at the relational response level are identified as active and having high learning outcomes, students who are at the multi-structural response level are identified as active and having moderate learning outcomes, students who are at the Uni-structural response level are identified as active and having moderate learning outcomes, and students who are at the pre-structural response level are identified as less active and having low learning outcomes. Thus, it can be interpreted that students' responses based on solo taxonomy in mathematics learning affect activeness and learning outcomes.

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Section: Introductionsupporting

confidence: 86%

An Identification of Students’ Responses Based on Solo Taxonomy in Mathematics Learning Toward Learning Activities and Learning Outcomes

Kaharuddin

Hajeniati

2020

ajpm

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“…The systems must be developed that can predict learners' achievement and identify students at risk (Siemens, 2013;Casey & Azcona, 2017). One of the supportive intervention components presented to the students within the scope of this study is to prediction their achievement status based on their interactions.…”

Section: Resultsmentioning

confidence: 99%

An intervention engine design and development based on learning analytics: the intelligent intervention system (In2S)

Şahi̇n

Yurdugül

2019

Smart Learn. Environ.

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In this study, an intervention engine based on learning analytics was designed and developed. The intervention engine is named the Intelligent Intervention System (In 2 S). Within the scope of this research; In 2 S system and its components have been introduced, and the system is evaluated based on learners' views. In 2 S includes three types of intervention that are instructional, supportive, and motivational intervention. The instructional intervention was structured based on assessment tasks. The supportive and motivational interventions were structured based on the learning experiences of the learners. Signal lights (red, yellow, and green) are presented to the learners for each assessment task as an instructional intervention. Supportive intervention is presented to the learners via the dashboard. In the context of motivational intervention, elements of gamification as a leader board, badges, and notifications have been used. In order to obtain the learner's views about the In 2 S, semi-structured interviews were conducted with the learners who had a previous learning experience with the system. The learning environment was evaluated based on their views. Learners had a nine-week learning experience in the e-learning environment. Then, eight students who used the system most actively and eight students who used the system most passively were selected for focus group interviews.. According to the findings, it was seen that the learners who use the intervention engine indicated that the system is useful and want to use it in the context of other courses.

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“…Educational institutes generate massive amounts of student data which can broadly be categorized as descriptive, behavioral, attitudinal and interactional (Meghji, Mahoto, Unar & Shaikh, 2018). In recent years, there has been growing interest in analyzing this data to better understand student learning behavior (Casey & Azcona, 2017). The prediction and understanding of student performance are essential for the establishment of a student centric learning environment; if educators can predict student performance, they can have mechanisms in place to ensure this performance constantly improves or, at any rate, does not fall beneath an acceptable threshold.…”

Section: Introductionmentioning

confidence: 99%

Predicting Student Academic Performance using Data Generated in Higher Educational Institutes

Meghji¹,

Mahoto²,

Unar³

et al. 2019

3C Tecnología

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The analysis of data generated by higher educational institutes has the potential of revealing interesting facets of student learning behavior. Classification is a popularly explored area in Educational Data Mining for predicting student performance. Using student behavioral data, this study compares the performance of a broad range of classification techniques to find a qualitative model for the prediction of student performance. Rebalancing of data has also been explored to verify if it leads to the creation of better classification models. The experimental results, validated using well-established evaluation matrices, presented potentially significant outcomes which may be used for reshaping the learning paradigm.

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Utilizing student activity patterns to predict performance

Cited by 59 publications

References 18 publications

An Identification of Students’ Responses Based on Solo Taxonomy in Mathematics Learning Toward Learning Activities and Learning Outcomes

An Identification of Students’ Responses Based on Solo Taxonomy in Mathematics Learning Toward Learning Activities and Learning Outcomes

An intervention engine design and development based on learning analytics: the intelligent intervention system (In2S)

Predicting Student Academic Performance using Data Generated in Higher Educational Institutes

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