An Learning Situation Early Warning Method Based on Linear Regression

Yao, Dunhong; Deng, Xinyang

doi:10.1109/icbdie50010.2020.00089

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Interestingly, HEIs are now integrating such features or element (flexibility) in the design of the several educational frameworks or learning platforms and curriculum. For instance, the MFD model [77], [87], [107] described in this paper, and the other hybrid models for learning [21], [108], [109] that didactically allows the students to choose and learn in either or both remote and in-person settings based on their learning needs and circumstances. Moreover, the flexible and digital models for learning is perceived to help tackle the problem of ''rigidity'' of the curriculums that has been identified as one of the main educational problems in both the literature and in practice [27], [30], [75], [82].…”

Section: If You Are Going To Enter With [Instructor] They Will Give Y...mentioning

confidence: 99%

A Text Mining and Statistical Approach for Assessment of Pedagogical Impact of Students’ Evaluation of Teaching and Learning Outcome in Education

Okoye

Daruich

et al. 2023

IEEE Access

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Technology-enhanced learning (TEL) is now at the heart of teaching and learning process in many higher education institutions (HEIs). Today, educators are faced with the challenges of pedagogically specifying what tools, methods, and technologies are used to support the teachers and students, and to help maintain/sustain a continuous education and practices. This study shows that there is an opportunity in the use of (educational) datasets derived about the teaching and learning processes to provide insights for fostering the education process. To this effect, it analyzed the students' evaluation of teaching (SET) dataset (n = 471968) collected within a higher education setting to determine prominent factors that influences the students' performance or the way (TEL-based) education is being delivered, including its didactical impact and implications for practice. Theoretically, the study employed a mixed methodology grounded on integration of the Data-structure approach and Descriptive decision theory to study the rationality behind the students' evaluation of the teaching and performance. This was done through the Textual data quantification (qualitative) and Statistical (quantitative) analysis. Qualitatively, the study applied the Educational Process and Data Mining (EPDM) model (a text mining method) to extract the different sentiments and emotional valence expressed by the students in the SET, and how those characteristically differ based on the period and type of evaluation they have completed (between 2019 to 2021). For the quantitative analysis, the study used a multivariate analysis of covariance (MANCOVA) and multiple pairwise comparisons posthoc tests to analyze the quantified information (average sentiment and emotional valence) extracted from the SET data to determine the marginal means of effect the different SET types and evaluation period have on the students' learning outcomes/perception about the teaching-learning process. In addition, the study empirically discussed and shed light on the implications of the main findings for TEL-based Education, particularly implemented by the HEI during the analyzed periods. The scholastic indicator from the study shows that while the flexible digital models or instructional methods are effective for continuous education,The associate editor coordinating the review of this manuscript and approving it for publication was Biju Issac .

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Section: If You Are Going To Enter With [Instructor] They Will Give Y...mentioning

confidence: 99%

A Text Mining and Statistical Approach for Assessment of Pedagogical Impact of Students’ Evaluation of Teaching and Learning Outcome in Education

Okoye

Daruich

et al. 2023

IEEE Access

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“…Meantime, through SPSS analysis of interactive content, it is evaluated that the SPOC course plays an active role in providing students with social space for collaborative learning, promoting meaningful and task‐related interactions, and creating social interaction to stimulate students' learning motivation. In addition, some researchers used data mining technology to normalize each data item in the teaching process and then constructed the calculation model of early warning value, which was used to dynamically monitor the change of early warning value of students in the classroom implementation process, and to warn and help students [19, 73].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

SPOC online video learning clustering analysis: Identifying learners' group behavior characteristics

Lü

et al. 2023

Comp Applic In Engineering

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With the widespread of Small Private Online Courses (SPOC) in colleges and universities, course organizers who provide high-quality personalized course activities need to understand learners' learning status and characteristics, and then optimize the course teaching. However, little research has been done on different learners' group behavior characteristics, such as which indicators can reflect learner groups' behavior, and what are the typical behavior characteristics of different learner groups. In this work, we established a Python Language Foundation self-built SPOC course, and 109 undergraduates' learning behavior data were collected and analyzed. From 74-dimensional behavior log data consisting of 72 video-viewing, course video score, and comprehensive score, Principal Component Analysis was performed to reduce dimensionality. Agglomerative hierarchical clustering was used to divide learners into different categories, and the results showed that 15 groups are clustered. According to the analysis of the four indicators for group characteristics, which are the completion and viewing-stability of task-point videos, unit test excellence, and comprehensive score, it is identified into five primary types, including positive type, regular type, negative type, seminegative type, and a fluctuation type. Experiments conducted on a real data set across different academic years and courses show that the proposed approach has better clustering accuracy and practicability. It is expected that this work may be used to strengthen the personalized learning support services system in educational institutions and develop a tool that integrates exploration and analysis work onto the web platform.

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Strengthening global health security through health early warning systems: A literature review and case study

Alahmari,

Almuzaini,

Alamri

et al. 2024

Journal of Infection and Public Health

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An Learning Situation Early Warning Method Based on Linear Regression

Cited by 5 publications

References 5 publications

A Text Mining and Statistical Approach for Assessment of Pedagogical Impact of Students’ Evaluation of Teaching and Learning Outcome in Education

A Text Mining and Statistical Approach for Assessment of Pedagogical Impact of Students’ Evaluation of Teaching and Learning Outcome in Education

SPOC online video learning clustering analysis: Identifying learners' group behavior characteristics

Strengthening global health security through health early warning systems: A literature review and case study

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