The influences of a two-tier test strategy on student learning: A lag sequential analysis approach

Yang, Tzu-Chi; Chen, Sherry Y.; Hwang, Gwo‐Jen

doi:10.1016/j.compedu.2014.11.021

Cited by 54 publications

(28 citation statements)

References 40 publications

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“…Following this clustering, the students' access behavior in each of the two IED clusters were identified. To do that, the distribution and frequency of their access behaviors in terms of Inquiry phases was harvested and processed, following the methodology described in Yang et al (2015). The harvested data were normalized for each cluster in terms of the student cardinality in each cluster.…”

Section: Evaluation Results: Investigation Strandmentioning

confidence: 99%

“…For this strand, the previously described clustering of the IED was again exploited. To answer this strand, lag Sequential Analysis was employed to elicit statistically significant student access behaviors when engaging with the different phases of the IED (Yang et al, 2015). More specifically, lag Sequential Analysis was utilized to study the flow in which students accessed Inquiry phases and draw conclusions about how these flows are related to the GDI.…”

Section: Evaluation Results: Investigation Strandmentioning

confidence: 99%

“…Following the methodology of lag Sequential Analysis, (e.g., Yang et al, 2015), the frequency transition tables were calculated, namely the frequency of students' access behavior in each Phase immediately following another phase. Based on this, the resulting adjusted residuals tables were calculated based on the z-score (Table 6 and Table 7) (Wang et al, 2014), so as to examine the students' behavioral patterns of accessing different Inquiry phases and identify potential statistically significant differences in each cluster.…”

Section: Evaluation Results: Investigation Strandmentioning

confidence: 99%

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Using educational data from teaching and learning to inform teachers’ reflective educational design in inquiry-based STEM education

Sergis

Sampson

Rodríguez‐Triana

et al. 2019

Computers in Human Behavior

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Highlights • Tool-supported Guidance is essential for effective Inquiry-based education • Teaching and Learning Analytics (TLA) can support teachers provide appropriate Guidance • The TLA method and supporting tool provides analyses of the level of Guidance in Inquiry-based scenarios • Analyses of the design can be investigated against customizable learners' data and profiles • Insights from these combined analyses could help teachers improve their teaching designs Abstract: Science, Technology, Engineering and Mathematics (STEM) education is recognized as a top priority for school education worldwide and Inquiry-based teaching and learning is identified as one of the most dominant approaches. To effectively engage individual students in Inquiry tasks, appropriate guidance needs to be provided, usually by combining different digital tools such online labs, data analysis tools and modelling tools. This is a cumbersome task for teachers to perform manually since it involves (a) assessing during the education design, the type and level of tool-supported guidance to be provided to students and (b) potentially refining this level and types to meet the guidance needs of individual students based on educational data from the delivery of the educational design. Thus, in our research we target to investigate how to support this process with educational data analytics methods and tools from both the design and the delivery of educational designs, that inform teachers' decision making for systematic reflection. To this end, the contribution of this paper is the design and evaluation of a novel "Teaching and Learning" Analytics method and supporting research prototype tool, extending the scope of purely learning analytics methods, to (a) analyze inquiry-based educational designs in terms of the tool-supported guidance they offer and (b) relate these analyses to students' educational data that are already being collected by existing learning analytics systems, so as to increase teachers' awareness and understanding and scaffold their reflection. A two-layer evaluation methodology was adopted to evaluate both the capacity of our method to analyze educational designs in terms of appropriate guidance as well as to investigate whether the insights generated by the method offer statistically significant indicators that impact students' activity during the delivery of these educational designs. The results obtained, based on real-life educational data, argue that the proposed method and tool can support teachers to accurately analyse Inquiry-based educational designs and receive meaningful insights to improve and tailor students' learning experiences. The insights of this work aim to contribute in the research field of cognitive data analytics for teaching and learning, by investigating new ways to combine analyses of the educational design and the students' activity, so as to inform teachers' reflective decision making from a holistic perspective.

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Section: Evaluation Results: Investigation Strandmentioning

confidence: 99%

Section: Evaluation Results: Investigation Strandmentioning

confidence: 99%

Section: Evaluation Results: Investigation Strandmentioning

confidence: 99%

See 1 more Smart Citation

Using educational data from teaching and learning to inform teachers’ reflective educational design in inquiry-based STEM education

Sergis

Sampson

Rodríguez‐Triana

et al. 2019

Computers in Human Behavior

View full text Add to dashboard Cite

show abstract

“…Moreover, teachers can gain a deeper understanding of students’ understanding of concepts using the two‐tier test strategy. In addition, teachers could cross‐check that the students truly understand the concepts of the learning content (Yang, Chen, & Hwang, ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, the two‐level test strategy has been widely used in various learning areas, including thermodynamics (Siswaningsih, Firman, & Khoirunnisa, ), mathematics (Lin, ), natural science (Adodo, ; Chu, Hwang, Tsai, & Tseng, ), chemistry (Bayrak, ), programming languages (Yang et al ., ; Yang, Hwang, Yang, & Hwang, ), genetics (Tsui & Treagust, ) and astronomy (Kanli, ). These studies have indicated that the strategy is beneficial to students in learning in different areas, and especially in terms of students’ learning achievement.…”

Section: Literature Reviewmentioning

confidence: 99%

Effects of applying a VR‐based two‐tier test strategy to promote elementary students’ learning performance in a Geology class

Chang¹,

Hsu²,

Kuo³

et al. 2019

Brit J Educational Tech

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Science and technology are driving people’s life changes, including education and the environment. Many scholars have attempted to import technology into the classroom to help students learn in different subjects. However, students often need assistance with unfamiliar learning approaches and learning environments. This study proposed a non‐immersive virtual reality (VR) guidance system combined with a two‐tier strategy to help students learn geology knowledge. Two groups of students used different learning approaches: the experimental group students were guided by the two‐tier test VR guidance system, and the control group students learned with the conventional VR guidance system. According to the experimental result, the two‐tier test VR guidance system not only improved the students’ learning achievement in natural science, but also enhanced their learning motivation. In addition, according to the sequential results, we found that VR learning materials or environmental resources can help students answer questions and solve problems more effectively.

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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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The influences of a two-tier test strategy on student learning: A lag sequential analysis approach

Cited by 54 publications

References 40 publications

Using educational data from teaching and learning to inform teachers’ reflective educational design in inquiry-based STEM education

Using educational data from teaching and learning to inform teachers’ reflective educational design in inquiry-based STEM education

Effects of applying a VR‐based two‐tier test strategy to promote elementary students’ learning performance in a Geology class

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

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