Tuesday, 21 April 2020

Scientometric Portal

 Source: https://sites.google.com/site/hjamali/scientometric-portal

Scientometric Portal

This page serves as a gateway to scientometric-related materials and resources. If you are aware of anything that should be added to this page please let me know.

Tools and Software

General network and graph analysis and visualization  

  • You might find this Wikipedia list of social network analysis software useful. 
  • AGNA. free Java-based software for SNA, sociometry and sequential analysis. Its name stands for Applied Graph and Network Analysis.
  • CFinder. A is a free software for finding and visualizing overlapping dense groups of nodes in networks, based on the Clique Percolation Method (CPM). 
  • Cytoscape. A free Java-based open-source software that although originally designed for bioinformatics research, now it is a general platform for complex network analysis and visualization. Cytoscape core distribution provides a basic set of features for data integration and visualization. Additional features are available as plugins.
  • GeoVIZ. A free toolkit for systematic analysis of spatial, temporal, and attribute data sets. It allows analysts to discover previously hidden patterns in data, moving from spatial patterns to statistical patterns and back again by mixing and matching data visualization components to quickly construct custom analysis tools. It provides a large selection of mapping and statistical graphing components for depicting univariate and multivariate data in dynamically linked views.
  • Gephi. A free open-source interactive visualization and exploration platform for all kinds of networks and complex systems, dynamic and hierarchical graphs.
  • Graphviz. A free open-source graph visualization software. Its main applications are networking, bioinformatics,  software engineering, database and web design, machine learning, and in visual interfaces for other technical domains. It takes descriptions of graphs in a simple text language, and make diagrams in useful formats, such as images and SVG for web pages, PDF or Postscript for inclusion in other documents; or display in an interactive graph browser.
  • GUESS. An free exploratory data analysis and visualization tool for graphs and networks. It can import standard formats (Pajek, GML) and export a wide variety of image types (GIF, PNG, EPS, PDF, JPG, SVG...). Because it is Jython/Java-based, users can also construct your own applications and applets without much coding.
  • igraph. A free software package for creating and manipulating undirected and directed graphs. It includes implementations for classic graph theory and also implements algorithms for some recent network analysis methods, like community structure search.
  • KeyPlayer. A free software  for identifying an optimal set of nodes in a network for one of two basic purposes: (a) crippling the network by removing key nodes, and (b) selecting which nodes to either keep under surveillance or to try to influence via some kind of intervention. Written by Steve Borgatti.
  • InFlow. commercial software for Social Network Analysis & Organizational Network Analysis.
  • MapEquation. Free algorithm and software for detecting communities in large networks.
  • Multinet. A free data analysis package that can be used for ordinary data (in which you have a file that has one line of data for each case) and for network data (in which there are two files -- the "node" file describes the individuals and the "link" file describes the connections between individuals). [It hasn't been updated for a long time].
  • NetDraw. A free program written by Steve Borgatti for visualizing both 1-mode and 2-mode social network data. It can read UCINET system files, UCINET DL files, Pajek files, and its own VNA format. It exports networks as a metafile, jpg, gif and bitmap formats.
  • NetMiner. A commercial software tool for exploratory analysis and visualization of Network Data. It has 73 kinds of SNA modules and 23 kinds of visualization modules.
  • NetworkX. A  free Python-based open-source software for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. 
  • NodeXL. A free, open-source template for MS Excel to draw graphs and networks. Networks can be imported from and exported to a variety of file formats (e.g. GraphML, UCINet, Pajek, and matrix), and built-in connections for getting networks from Twitter, Flickr, YouTube, and your local email are provided. You can learn how to use it by reading the book by Hansen et al. (2010).
  • Pajek. A free Python-based open-source software for large networks analysis of visualization. It is probably the most popular network analysis software and largely used by experts in scientometrics. You can learn how to use it by reading the book by de Nooy et al. (2011).
  • prefuse. A free Java-based set of software tools for creating rich interactive data visualizations. Some of its features are Table, Graph, and Tree data structures supporting arbitrary data attributes, data indexing, and selection queries, and animation support.
  • Smart Local Moving (SLM) algorithm: A free Java-based open-source algorithm (implemented in Modularity Optimizer) for community detection (or clustering) in large networks. It maximizes a so-called modularity function and it has been successfully applied to networks with tens of millions of nodes and hundreds of millions of edges. You can read its paper here.
  • SNA package for R. A free range of tools for social network analysis with R, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.
  • SoNIA. or Social Network Image Animator is a free open-source Java-based package for visualizing dynamic or longitudinal network data.
  • StOCNET. free open-source software for advanced statistical analysis (based on probability model) of social networks.
  • Tulip. A free information visualization framework written by C++ dedicated to the analysis and visualization of relational data.
  • UCINet. A commercial social network analysis program developed by Steve Borgatti and colleagues and distributed by Analytic Technologies. UCINET works in tandem with freeware program called NetDraw for visualizing networks. NetDraw is installed automatically with UCINet.
  • Visone. free software for analysis and visualization of social network data.

 Scientometric and bibliometric analysis 

  • You might also find this Wikipedia comparison of research networking tools and research profiling systems useful.  
  • Bibexcel. Free software designed by Olle Persson to assist a user in analyzing bibliographic data, or any data of a textual nature formatted in a similar manner. The idea is to generate data files that can be imported to Excel or any program that takes tabbed data records, for further processing. It can be used for co-citation, bibliographic coupling, mapping and clustering analysis.
  • Bibliometrix R package. A free tool that provides various routines for importing bibliographic data from SCOPUS and Clarivate Analytics' Web of Science databases, performing bibliometric analysis and building data matrices for co-citation, coupling, scientific collaboration analysis and co-word analysis.
  • BiblioTool. It is a set of python scripts (open source) written by Sebastian Grauwin. They can read ISI data in CSV format and do some analyses including co-occurrence map and bibliographic coupling.
  • CiteSpace. A free Java-based software for visualizing and analyzing trends and patterns in the scientific literature. It is designed as a tool for progressive knowledge domain visualization. Its primary source of input data is ISI WoS. But it also provides some simple interfaces for obtaining data from PubMed, arXiv, ADS, and NSF Award Abstracts. It can be used to generate geographic map overlays viewable in Google Earth based on the locations of authors.
  • CitNetExplorer. A free Java-based software tool developed by Uni of Leiden for visualizing and analyzing citation networks of scientific publications. It allows citation networks to be imported directly from the Web of Science database. Citation networks can be explored interactively, for instance by drilling down into a network and by identifying clusters of closely related publications. 
  • CopalRed. (obsoleted) A free program written by Xavier Polanco for the analysis of scholarly publications and scientometric purposes for example for analysing and visualizing the network structure of a scientific field.
  • CRExplorer. Or Cited Reference Explorer is a free Java-based program that was primarily developed to identify those publications in a field, a topic or by a researcher which have been frequently cited. It is especially suitable to study the historical roots of this field, topic or researcher.
  • InCite Retrieve: a set of Python codes for retrieving journal impact factor values from InCite API.
  • InterDisciplinary Research (IDR). It's a free tool to measure and map interdisciplinary research. It creates overlay maps of science, as a method to explore the degree of interdisciplinarity of a set of publications. 
  • IN-SPIRE. A commercial software for exploring and visualizing textual data, including Boolean and “topical” queries, term gisting, and time/trend analysis tools. It can be used to explore technical and patent literature, marketing and business documents, web data, accident and safety reports, newswire feeds and message traffic, and more.
  • Headstart. A free open-source software to visualize readership data from Mendeley. It presents users with the main areas in the field and lets them zoom into the most important publications within each area. It is intended to give researchers that are new to a field a head start on their literature review (hence the name). It has been developed by P. Kraker.
  • HistCite(obsoleted) free software developed by E. Garfield to aid researchers in visualizing the results of literature searches in the Web of Science. It lets you analyze and organize the results of a search to obtain various views of the topic's structure, history, and relationships. It visualizes the citation network in a historical manner.
  • Loet Leydesdorff. A set of free DOS-based pieces of software to parse, transform and analyse bibliometrics data obtained from sources such as Scopus, ISI, and Google Scholar for analyses such as coauthorship, international, institutional, inter-city collaboration networks, co-word, co-citation and bibliographic analysis and so on. Although they do not include visualization tools, they prepare the data for the creation of relational databases and visualization by other tools such as Pajek. ISI.exe reads ISI data in txt format and generates files suitable for creating a relational database.
  • Network Workbench. A free Java-based large-scale network analysis, modelling and visualization toolkit for biomedical, social science and physics research. It includes specific features for bibliometric studies.
  • Publish or Perish. A free software program that retrieves and analyzes academic citations Google Scholar and calculates No of papers, citations, average No. of citations per paper and per author and per year as well as h-index, g-index, and some more metrics.
  • SAINT: (obsoleted) (Science Assessment Integrated Network Toolkit). It is open-source software for scientometrics analysis and one of the few packages that can be used to convert ISI data into a relational database (dbm or accdb or sql files). There is a forum to discuss the issues related to SAINT. The software is not available on its original website anymore.
  • ScientoPY, a free open source scientometric software. You can read about it in this paper
  • SciMAT. SciMAT (Science Mapping Anaylsis Tool) is a java-based open source (GPLv3) free software tool developed to perform a science mapping analysis under a longitudinal framework. SciMAT reads bibliographic data in different formats and creates a relational database in Sqlite 3 format and allows you to do different analyses. The advantage is that you can amend the data in the knowledgebase as you wish.
  • Sci2 Tool. A free Java-based modular toolset specifically designed for the study of science. It supports the temporal, geospatial, topical, and network analysis and visualization of scholarly datasets at the micro (individual), meso (local), and macro (global) levels. It has several visualization features.
  • Scientometric Project. A set of open-source Python scripts for some scientometric data analyses written by Theresa Velden.
  • Scopus API R code: This is some R code to query Scopus API and parse the results into a data frame. For instance, if you have a list of DOI and want to get citation data for them from Scopus.
  • Pybliometrics: Python-based API-Wrapper to access Scopus: A free Python library to cache and extract data from the Scopus, developed by M. E. Rose and J. R. Kitchin. You can read about it in this paper
  • Sitkis: (obsoleted) Sitkis is a free Java-based software tool developed exclusively for bibliometric analysis. Sitkis provides tools for extremely streamlined analysis of bibliometric networks. Read more about it here.
  • VOSviewer. A free Java-based program, primarily intended to be used for analyzing and visualizing bibliometric networks. It can create maps of publications, authors, or journals based on a co-citation network or to construct maps of keywords based on a co-occurrence network.
  • Web of Science API: a set of Python code to retrieve the times cited counts for DOIs and/or PMIDs.
  • Webometric Analyst: a free Windows-based program for altmetrics, citation analysis, social web analysis and webometrics, including link analysis, developed by Prof. Mike Thelwall.
For a list of some of the tools used in scientometrics studies see Borner et al (2010) and for comparison of some of these software packages see Cobo et al. (2011). 

Science Mapping Resources

  • Places & Spaces: Mapping Science. It is a collection of science maps and visualizations. It is exhibited in different places and they can be ordered.
  • Atlas of Science. This is a book by Katy Börner published by MIT press. It includes 500 colour illustrations of different science maps.
  • Excellence Mapping. This web application visualizes the scientific performance of institutions (universities or research-focused institutions) within specific subject areas (e.g. Chemical Engineering) as ranking lists and on maps.

Science Analysis Companies and Services

  • Academic Analytics. It is a provider of high-quality, custom business intelligence data and solutions for research universities in the United States and the United Kingdom. It helps universities identify their strengths and areas where improvements can be made.
  • Clarivate. It publishes Web of Knowledge and Web of Science and it also produces a few science analysis databases such as Journal Citation Reports, Science Watch, and Essential Science Indicators. WoS includes some analysis tools.
  • Elsevier. It is the publisher of Scopus database as well as SciVal which Is a suite of research tools that helps you evaluate, establish and execute your research strategies more effectively. SciVal Spotlight is a unique web-based strategic analysis tool that enables academic executives to make informed strategic decisions by measuring and evaluating an institution's research performance. It evaluates your institution's research output in a single interface. SciVal Funding is a web-based solution that gives research administrators and researchers in the pre-award stage access to current research funding opportunities and award information. It allows you to find the right funding opportunities and analyze the funding environment.
  • SCImago. Is a portal that includes the journals and country scientific indicators developed from the information contained in the Scopus. These indicators can be used to assess and analyze scientific domains.
  • Science Metrix. (now owned by Elsevier) It provides customized services in performance measurement and program evaluation using advanced bibliometric indicators and recognized quantitative and qualitative research methods. In 2010 it published a '30 Years in Science' report.
  • SciTech Strategies Inc. It mainly creates maps of science.

Journals related to Scientometrics

Books on Scientometrics

  1. Anderes, A. (2009). Measuring Academic Research: How to undertake a bibliometric study. Oxford: Chandos.
  2. Biagioli, M., & Lippman, A. (editors) (2020). Gaming the Metrics: Misconduct and Manipulation in Academic Research, Cambridge: MIT Press. 
  3. Borgman, C.L. (1990). Scholarly communication and bibliometrics: Sage Publications.
  4. Borner, K. (2010). Atlas of Science: Visualizing What We Know: MIT Press.
  5. Braam, R.R. (1991). Mapping of science: foci of intellectual interest in scientific literature: DSWO Press, University of Leiden.
  6. Braun, T. (Ed.). (2006). Evaluations of Individual Scientists and Research Institutions. Part I. Scientometrics Guidebooks Series: Akademiai Kiado Zrt.
  7. Braun, T. (Ed.). (2006). Evaluations of Individual Scientists and Research Institutions. Part II. Scientometrics Guidebooks Series: Akademiai Kiado Zrt.
  8. Braun, T. (2007). The Impact Factor of Scientific and Scholarly Journals: Its Use and Misuse in Research Evaluation: Akadémiai Kiadó.
  9. Braun, T. (2008). The Hirsch-index for evaluating science and scientists. Its uses and misuses: Akadémiai Kiadó.
  10. Braun, T., Bujdosó, E., & Schubert, A. (1987). Literature of analytical chemistry: a scientometric evaluation: CRC Press.
  11. Braun, T., Glänzel, W., & Schubert, A. (1985). Scientometric indicators: a 32 country comparative evaluation of publishing performance and citation impact: World Scientific.
  12. Cantú-Ortiz, F. J. (2017). Research Analytics: Boosting University Productivity and Competitiveness through Scientometrics. Auerbach Publications.
  13. Chiesa, V., & Frattini, F. (2009). Evaluation and performance measurement of research and development: techniques and perspectives for multi-level analysis: Edward Elgar.
  14. Cronin, B. (1984). The citation process: the role and significance of citations in scientific communication: Taylor Graham.
  15. Cronin, B., & Atkins, H.B. (Eds.). (2000). The Web of Knowledge: A Festschrift in Honor of Eugene Garfield: Information Today Inc.
  16. Cronin, B. & Sugimoto, C. (Eds). (2014) Beyond Bibliometrics : Harnessing Multidimensional Indicators of Scholarly Impact. Massaschussets, MIT Press.
  17. Cronin, B. & Sugimoto, C.R., (Eds.) (2015). Scholarly metrics under the microscope. Medford, NJ: Information Today.
  18. De Bellis, N. (2009). Bibliometrics and Citation Analysis: From the Science Citation Index to Cybermetrics. Lanham: Scarecrow Press.
  19. Devarajan, G. (1997). Bibliometric studies: Ess Ess Publications.
  20. Diodato, V.P. (1994). Dictionary of bibliometrics: Haworth Press.
  21. Egghe, L. (2005). Power Laws in the Information Production Process: Lotkaian Informetrics: Emerald Group Publishing Limited.
  22. Egghe, L., & Rousseau, R. (1990). Introduction to informetrics: quantitative methods in library, documentation and information science: Elsevier Science Publishers.
  23. Eom, S. (2009). Author cocitation Analysis: Quantitative Methods for Mapping the Intellectual Structure of an Academic Discipline. Hershey: Information Science Reference.
  24. Érdi, P. (2019). Ranking - The Unwritten Rules of the Social Game We All Play. New York: Oxford University Press. 
  25. Evered, D., & Harnett, S. (1989). The Evaluation of Scientific Research: Wiley.
  26. Gingras, Y. (2016). Bibliometrics and Research Evaluation: Uses and Abuses, Cambridge, MA: MIT Press.
  27. Geisler, E. (2000). The metrics of science and technology: Quorum Books.
  28. Harzing, A.W. (2010). The Publish Or Perish Book: Your Guide to Effective and Responsible Citation Analysis: Tarma Software Research.
  29. Hasan, N. (2010). Mapping the dynamics of world agricultural research output: A scientometric study LAP LAMBERT Academic Publishing.
  30. Hjerppe, R. (1980). An outline of bibliometrics and citation analysis, Royal Institute of Technology Library.
  31. Holden, G., Rosenberg, G., & Barker, K. (2006). Bibliometrics in social work: Haworth Social Work Practice Press.
  32. International Survey of Research University Faculty: Use of Bibliometric Ratings, Identifiers & Indicators (2017). Primary Resource Group.
  33. Leydesdorff, L. (2001). The Challenge of Scientometrics: The Development, Measurement, and Self-Organization of Scientific Communications: Universal-Publishers.
  34. Moed, H.F. (2017). Applied evaluative informetrics, Springer.
  35. Moed, H.F. (1989). The use of bibliometric indicators for the assessment of research performance in the natural and life sciences: aspects of data collection, reliability, validity, and applicability: DSWO Press.
  36. Moed, H.F., Glänzel, W., & Schmoch, U. (2004). Handbook of quantitative science and technology research: the use of publication and patent statistics in studies of S & T systems: Kluwer Academic Publishers.
  37. Nicholas, D., & Ritchie, M. (1978). Literature and bibliometrics: C. Bingley.
  38. Okubo, Y. (1997). Bibliometric indicators and analysis of research systems: methods and examples: OECD.
  39. Pǎces, V., Pivec, L., & Teich, A.H. (1999). Science evaluation and its management: IOS Press.
  40. Raan, A.F.J. (1988). Handbook of quantitative studies of science and technology: North-Holland.
  41. Raan, A.F.J., Nederhof, A.J., & Moed, H.F. (1989). Science and technology indicators: their use in science policy and their role in science studies: select proceedings of the First International Workshop on Science and Technology Indicators, Leiden, The Netherlands, 14-16 November 1988: DSWO Press, University of Leiden.
  42. Rana, M.S. (2010). Scientometric Study of Wild Mammal Research in India: Authorship, Distribution and Research Trend: LAP Lambert Academic Publishing
  43. Rao, I.K.R. (2010). Growth of Literature and Measures of Scientific Productivity: Scientometric Models, Ess Ess Publications.
  44. Roemer, R. C. & Borchardt, R. (2015). Meaningful Metrics: A 21st Century Librarian's Guide to Bibliometrics, Altmetrics, and Research Impact, ACRL. 
  45. Santo, A.E. (1978). A measure of the dimensions of interdisciplinarity of two applied sciences: a scientometric model: University of Wisconsin.
  46. Sinha, S. C. & Zhiman, A. K. (2001). Citation Analysis of Research Field and Information Technology Development. ESS ESS Publications. 
  47. Sugimoto, C.R., & Larivière, V. (2017). Measuring research: what everyone needs to know. Oxford: Oxford University Press. 
  48. Tattersall, A. (editor) (2015). Altmetrics: A practical guide for librarians, researchers and academics, Facet Publishing.
  49. Thelwall, M. (2016). Web indicators for research evaluation: A practical guide. Synthesis Lectures on Information Concepts, Retrieval, and Services. San Rafael, CA: Morgan & Claypool Publishers.
  50. Tijssen, R.J.W. (1992). Cartography of science: scientometric mapping with multidimensional scaling methods: DSWO Press, Leiden University.
  51. Tijssen, R.J.W., Leeuwen, T.N., & Raan, A.F.J. (2002). Mapping the scientific performance of German medical research: an international comparative bibliometric study: Schattauer.
  52. Todeschini, R., & Baccini, A. (2016). Handbook of bibliometric indicators: quantitative tools for studying and evaluating research. Wiley-VCH..
  53. Vinkler, P. (2010). The Evaluation of Research by Scientometric Indicators. Oxford: Chandos.
  54. Whitley, R., & Gläser, J. (2007). The changing governance of the sciences: the advent of research evaluation systems: Springer.
  55. Zhao, D. & Strotmann, A.(2015). Analysis and Visualization of Citation Networks, Morgan & Claypool Publishers.

Saturday, 18 April 2020

Should you Share your Published Articles on Academic Social Media?

Source: https://libraryblog.graduateinstitute.ch/category/information-literacy/copyright/

Should you Share your Published Articles on Academic Social Media?

Many scholars are confused and do not know if they can or should upload the pdfs of their articles on academic social media websites such as Researchgate or academia.edu. Our colleague Catherine Brendow tries to clear things up.
Continue reading “Should you Share your Published Articles on Academic Social Media?”

Where can you create researcher profiles for reference and networking?

Source: https://libraryblog.graduateinstitute.ch/2020/04/14/where-can-you-create-researcher-profiles-for-reference-and-networking/

Where can you create researcher profiles for reference and networking?

Many researcher profile platforms emerged over the past 20 years, such as ORCID, Google Scholar Citation Profile, Academia, etc. This article by Linda Leger aims to give you an overview of the most popular researcher profile tools.
Establishing an online presence and making your research visible is vital in the current world of academia. Researcher profiles show who you are, where you work and what your research is about. Some platforms provide additional functionalities such as metrics, alerts, or forums, and allow you to create networks of collaboration. For the purpose of this article, we have divided the platforms used to create researcher profiles in 3 categories depending on their characteristics:
  • Persistent Digital Identifiers (PDIs)
  • Academic Networking
  • Other Author Profile Pages
More categories could be added that we do not address here, including generic networking platforms (Twitter, LinkedIn, …) and tools for reference and citation management (Mendeley, …).

Persistent Digital Identifier (PDIs)

These platforms provide a univocal way to identify a researcher. You should create a persistent digital identifier mainly to avoid confusion of names between homonyms or to ensure continuity in the event of a name change or variations in the ways your name is mentioned. Besides providing with this unique identifier, these platforms help you to manage your publication list, giving an overview of who you are and what your research is about.
This category can be divided in commercial and noncommercial persistent identifiers.

Academic Networking Platforms

The main purposes of these platforms is to connect researchers with common interests and let them share information. These platforms allow you to:
  • create a profile that summarizes your research
  • reference your publications so the others can find them 
  • find and follow other researchers so you can receive the automatic updates of their new publications
  • see platform-specific metrics that indicates the reach you have on those sites.
The most known and used platforms of this kind are ResearchGate and Academia.edu, which are both commercial. They should be used to build your network, but not to be the main host for your research papers.

Other Author Profile Pages

In this category we find mainly databases or subject repositories where you can create an author profile webpage. The databases of this category differ from the Academic Social Networks because they do not offer social functionalities such as the discussion board and network-building.
The most famous is the Google Scholar Citation Profile. Since Google Scholar is the starting point for most scholars’ research, you can use this to showcase for your publications. You can also keep track of your citations with a graph over time and receive notifications when new publications by another researcher you are interested in are added on Google Scholar.
Subject repositories such as Social Sciences Research Network (SSRN) and Research Papers for Economics (REPEC) also let you create an author profile page. This would help you to make your papers accessible through three channels: the SSRN e-library, which is well-known in the fields of economics and law; Google, which indexes this repository; and alerts sent to users. As in the case of Google Scholar Citation Profile, these databases provide metrics to follow the interest others have in your research.
Google Scholar is owned by Alphabet, and SSRN by Elsevier/RELX since 2016. REPEC is a collaborative project maintained by volunteers.

In conclusion

Researcher profiles can be a good way to promote your research and to create networks of collaboration, and you should not restrict yourself to a single platform.
You will find more information about these researcher profile platforms on our dedicated libguide. If you have any further question on the subject, you can contact Isabelle Vuillemin-Raval and Linda Leger.

Still looking for information on how to share and preserve your publications durably? Read for more on open access repositories such as the Graduate Institute’s.

Sunday, 12 April 2020

The Effect of STEM Project Based Learning on Self-Efficacy among High-School Physics Students

Source: http://www.tused.org/index.php/tused/article/view/876

Cite as: Jamali, S. M., Samsudin, M. A., Zain, A. N. M., & Ale Ebrahim, N. (2020). The Effect of STEM Project Based Learning on Self-Efficacy among High-School Physics Students. Journal of Turkish Science Education, 16(1), 94-108. doi:10.36681/tused.2020.15

The Effect of STEM Project Based Learning on Self-Efficacy among High-School Physics Students

  • Mohd Ali SAMSUDIN
  • Seyedh Mahboobeh JAMALI
  • Ahmad Nurulazam MD ZAIN
Keywords: Project-Based learning (PjBL), science, technology, engineering and mathematics (STEM), physics education, problem-solving, performance evaluation, bibliometrics


Science, Technology, Engineering and Mathematics (STEM) Project-Based Learning (PjBL) is increase effectiveness, create meaningful learning and influence student attitudes in future career pursuit. There are several studies in the literature reporting different aspects of STEM into a PjBL pedagogy. However, the effect of implementing STEM PjBL in terms of improving students’ skills in self-efficacy levels in physics mechanics at high school level has not been demonstrated as expected in the previous literature. This study followed a quasi-experimental research method. Bandura’s social cognitive theory is used to assess and compare the effect of STEM PjBL with conventional teaching method on students’ self-efficacy level in learning physics among over 100 high school students. The result illustrated that STEM PjBL improve students’ self-efficacy to solve physics problem. Also, the study proposes a guideline for future research.