This map shows the geographic impact of Ju Ling Shih's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ju Ling Shih with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ju Ling Shih more than expected).
This network shows the impact of papers produced by Ju Ling Shih. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ju Ling Shih. The network helps show where Ju Ling Shih may publish in the future.
Co-authorship network of co-authors of Ju Ling Shih
This figure shows the co-authorship network connecting the top 25 collaborators of Ju Ling Shih.
A scholar is included among the top collaborators of Ju Ling Shih based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Ju Ling Shih. Ju Ling Shih is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Shih, Ju Ling, et al.. (2020). An exploration of students’ computational thinking performance in a scenario-based robotic learning game. 99–100.1 indexed citations
2.
Shih, Ju Ling, et al.. (2019). Exploring the role of algorithm in elementary school students’ computational thinking skills from a robotic game. 217–222.
3.
Shih, Ju Ling, et al.. (2019). Effects of the interdisciplinary robotic game to elementary school students’abilities of computational thinking and STEM. 95–103.
4.
Shih, Ju Ling, et al.. (2018). Analysing Group Dynamics of a Digital Game-Based Adventure Education Course. Educational Technology & Society. 21(4). 51–63.1 indexed citations
5.
Shih, Ju Ling, et al.. (2017). The design and evaluation of a STEM interdisciplinary game-based learning about the great voyage. 546–554.1 indexed citations
6.
Shih, Ju Ling, et al.. (2016). Advancing Adventure Education Using Digital Motion-Sensing Games.. Educational Technology & Society. 19(4). 178–189.7 indexed citations
Shih, Ju Ling, et al.. (2014). Using Instructional Pervasive Game for School Children's Cultural Learning.. Educational Technology & Society. 17(2). 169–182.20 indexed citations
9.
Shih, Ju Ling, et al.. (2014). The Integration of Concept Mapping in a Dynamic Assessment Model for Teaching and Learning Accounting. Educational Technology & Society. 17(1). 141–153.15 indexed citations
Shih, Ju Ling, et al.. (2013). The instructional application of augmented reality in local history pervasive game.1 indexed citations
14.
Shih, Ju Ling, et al.. (2011). The learning effectiveness of pervasive game integrated with inquiry-based navigation system. 414–421.1 indexed citations
15.
Hwang, Gwo Jen, et al.. (2010). A Decision-Tree-Oriented Guidance Mechanism for Conducting Nature Science Observation Activities in a Context-Aware Ubiquitous Learning Environment. Educational Technology & Society. 13(2). 53–64.65 indexed citations
16.
Shih, Ju Ling, et al.. (2010). An Inquiry-based Mobile Learning Approach to Enhancing Social Science Learning Effectiveness. Educational Technology & Society. 13(4). 50–62.196 indexed citations
Shih, Ju Ling, et al.. (2009). Using PDA to enhance social science learning with inquiry-based strategies. 543–550.1 indexed citations
19.
Shih, Ju Ling, et al.. (2009). Analysis of student attitudes for participating in a context-aware ubiquitous learning activity with repertory grid approach. 255–259.
20.
Shih, Ju Ling & Hui Su. (2008). Analyzing children's cognitive activities in digital problem-solving learning games "william adventure": An in-depth case study. 651–658.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.