Using Learning Analytics and Visualization Techniques to Evaluate the Structure of Higher Education Curricula

Barbosa, Artur Mesquita; Araujo, Nilo; Pordeus, João Paulo; Santos, Emanuele

doi:10.5753/cbie.sbie.2017.1297

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…In , a notion called curriculum mining is brought up and the curriculum data is directly targeted. In synthetic control method is proposed to build a linear model describing the relation between courses based on student performance information. It can also predict students’ grades in a specific course based on their previous grades.…”

Section: Related Workmentioning

confidence: 99%

A data‐mining‐based approach to informed decision‐making in engineering education

Yahya

Osman

2019

Comp Applic In Engineering

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In outcome‐based academic programs, Program Educational Objectives (PEOs) and Student Outcomes (SOs) are two cores around which all programs’ components and processes revolve. Needless to say, the PEOs‐SOs mapping is very critical for program success and, therefore, a deep understanding of PEOs, SOs, and their intra/inter‐correlations is very important for effective program's decisions making. In this context, this paper proposes a data mining‐based approach to discover hidden knowledge of PEOs, SOs, and their mapping and correlations in engineering programs. More specifically, the proposed approach employs association rule mining techniques to generate association rules, among and between PEOs and SOs, from PEOs‐SOs mapping data of a set of engineering programs, which can be then filtered and manipulated to discover the desired knowledge. To this end, a set of 152 self‐study reports of engineering programs, accredited by American Board for Engineering and Technology‐Engineering Accreditation Commission (ABET‐EAC), are collected and the mapping data between their PEOs and ABET‐EAC SOs are extracted. The dataset is processed and transformed into a representation suitable for association rules mining. This involves developing a set of PEOs labels, annotating data with PEOs labels, and extracting three target datasets. Apriori algorithm is then applied to each dataset to generate three sets of association rules. The generated association rules are then filtered and manipulated to discover the knowledge of PEOs, ABET‐EAC SOs, and their mapping and correlations. Finally, a discussion on the informativeness of the discovered knowledge for the decisions making in engineering education is given.

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Section: Related Workmentioning

confidence: 99%

A data‐mining‐based approach to informed decision‐making in engineering education

Yahya

Osman

2019

Comp Applic In Engineering

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“…One factor that also plays a significant role in the development of students' knowledge and their performance is the structure of the curriculum [17]. We also proposed a data mining technique [18] that evaluates a curriculum's structure based on academic data collected from the undergraduate students. Our approach is based on the Synthetic Control Method (SCM), which has In the model, Databases II is more influenced by Databases I, being consistent with the official curriculum.…”

Section: Educationmentioning

confidence: 99%

“…Our approach is based on the Synthetic Control Method (SCM), which has In the model, Databases II is more influenced by Databases I, being consistent with the official curriculum. Source: [18] been successfully applied in previous studies in social science and economics [19], [20]. The SCM builds a linear model describing the relation between courses based on a dataset of student performance information.…”

Section: Educationmentioning

confidence: 99%

Visual Analytics Research at Universidade Federal do Ceará

Santos

Neto

Gomes

et al. 2021

Anais Estendidos Da XXXIV Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2021)

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In this paper, we describe the research in visual analytics that is a partnership between two research groups at the Federal University of Ceara, CRAB – Computer Graphics, Virtual Reality, Animation, and Visualization Group and Insight Data Science Lab. This fruitful collaboration joined CRAB’s expertise in Visualization with Insight Lab’s expertise in Data Science to bring about valuable research and development projects in the public and private sector in many areas, including crime analysis, mobility data visualization, education, and games. We briefly describe our main projects in these areas and their social, technological, and scientific contributions in the following sections.

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“…We published a previous work entitled "Vis2Learning: A scenariobased guide of recommendations for building edu cational data visualizations" (Macedo et al, 2020) where we discuss details about the creation process, validation stages and evaluation of Vis2Learning by the lens of the visualiza tion users. The Vis2Learning has 15 scenarios where, for each scenario, we provided: the recommended chart and its characteristics; application examples; and guidelines to en hance the user interaction with each chart.…”

Section: Introductionmentioning

confidence: 99%

“…The Vis2Learning has 15 scenarios where, for each scenario, we provided: the recommended chart and its characteristics; application examples; and guidelines to en hance the user interaction with each chart. In this article, we present an extended version of Macedo et al (2020). The novel contributions in this article are: (1) an expanded ver sion of the analysis of the participants' profile; (2) a new correlation analysis between the participants' experience and the evaluation results; and (3) an expanded discussion about cases where the participants declared a low level of agree ment about the charts created based on Vis2Learning.…”

Section: Introductionmentioning

confidence: 99%

Building information visualization of e-learning data with Vis2Learning guidelines

Paiva

Gasparini

Zaina

2022

JIS

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Information Visualization provides techniques to make better charts that enhance human perception about patterns in data and consequently support the user interpretation. In the educational area, visualizations help professionals to analyze a great amount of data to inform decisions to improve the learningteaching process. The literature has shown that there is a gap in the development of educational data visualizations that fulfill enduser needs. This paper presents Vis2Learning: a scenariobased set of guidelines for the development of visualizations in the elearning context. Vis2Learning provides a set of scenarios from which educational data visualizations can be developed, for each scenario, we provide the recommended chart, its aim, characteristics and examples of its application in the elearning context. Besides, we provide a set of guidelines to improve users’ interaction with each chart. We applied an online questionnaire with 34 endusers (Brazilian teachers), evaluating visualizations that were created by using the Vis2Learning. The results reveal: (1) the visualizations, based on Vis2Learning, were more suitable to be applied in the elearning context; (2) some nontraditional visualization formats are difficult to interpret by users who did not have previous experience with visualizations in the elearning context; and (3) experience in teaching is not strictly related to knowledge of charts about educational data.

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Using Learning Analytics and Visualization Techniques to Evaluate the Structure of Higher Education Curricula

Cited by 4 publications

References 6 publications

A data‐mining‐based approach to informed decision‐making in engineering education

A data‐mining‐based approach to informed decision‐making in engineering education

Visual Analytics Research at Universidade Federal do Ceará

Building information visualization of e-learning data with Vis2Learning guidelines

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