Roblox is a 3D online platform for users to play and create own multiplayer games. It was created in 2006, with release for IOS in 2013 and Android in 2014 and XBox One in 2015. Games are coded under an object-oriented programming LUA Language to manipulate the environment of the game.
it host occasionally real-life events and award ceremony which also functions as a fundraiser.
In 2020 reached a large number of users from 35 million to 150 million in 2021… Due to the pandemic its valuation increased from $4 billion to $29.5 billion. Similar effect experienced by the majority of gaming industry as players in particular children below 16 are spending more time indoors playing video games.
Roblox has a community of educators to inspire children’s creativity, curiosity and entrepreneurship.
Roblox allows both the import of files with own formats (.rbxm) and generics formats that the video game industry usually works with (.obj, .fbx, .stl, etc)
Scientific Studies about Roblox indicates both drivers ( educational tour) and barriers (uncensored users, bullying and unsafe platform for children )
Define your topic. Do you have central question you want to answer?
Narrow down what you want to research. Can you focus more deeply, rather than skimming the surface of your topic?
Structure your topic into key concepts ( themes) to make it easier to search and look up information
Use your learning material to identify key authors or theories that relate to the themes and make them your starting point
Do your learning material suggest any further reading? If so, track it down
Use an online library and open repositories to locate academic opinion and theory
Use search engine for scientific literature (academic research database):
Scopus is one of the two big commercial, bibliographic databases that cover scholarly literature from almost any discipline. Beside searching for research articles, Scopus also provides academic journal rankings, author profiles, and an h-index calculator.
Web of Science also known as Web of Knowledge is the second big bibliographic database. Usually, academic institutions provide either access to Web of Science or Scopus on their campus network for free
For education sciences, ERIC is the number one destination. ERIC stands for Education Resources Information Center, and is a database that specifically hosts education-related literature.
IEEE Xplore is the leading academic database in the field of engineering and computer science. It’s not only journal articles, but also conference papers, standards and books that can be search for.
ScienceDirect is the gateway to the millions of academic articles published by Elsevier. 2,500 journals and more than 40,000 e-books can be searched via a single interface.
The DOAJ is very special academic database since all the articles indexed are open access and can be accessed freely of charge.
Google Scholar is the clear number one when it comes to academic search engines.
Microsoft Academic takes a different approach and generates for each paper that is indexed an overview page that allows to easily explore top citing articles and references of the article
CORE is an academic search engine dedicated to open access research papers.
Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need any more to query all those resources separately!
Semantic Scholar is the new kid on the block. It’s mission is to provide more relevant and impactful search results using AI powered algorithms that find hidden connections and links between research topics.
ORGANIZE YOUR OWN LIBRARY Use the browser extension to import automatically your paper into your digital library, then:
Organise your literature using a scientific content manager (Mendeley, Zotero, Paperpile): store any paper copies in folders and files, grouped into themes
Read and annotate the literature you have sourced
Establish a criteria including a code system to tag only relevant literature
Tag only the relevant literature using the key themes you have identified
There are various software tools and cloud-based applications to support individual research such as ATLAS.ti; Dedoose; MAXqda; NVivo and NVivo 9 Server; QDA Miner; and Transana… However, the big challenge is to find a good platform for collective research.
A few solutions proposed by teams are: 1. Merging software projects and the completed work, after working individually.
Upload a PDF to your Google Drive (New>File upload, or click-and-drag the PDF into your Google Drive).
Click the PDF to preview it.
(Optional) Click the share button in the upper right to add other people, or get a link to share.
Click on the annotate icon in the upper right to start adding notes. Highlight text or illustrations throughout the document to comment on them.
The ability to annotate PDFs, or at least open in a PDF reader that will, without having to download the file, annotate, and upload.
Google, Asana, Trello
2.Ways to collaborate in NVivo
Many projects involve multiple researchers working together—NVivo provides two ways to approach collaboration:
Share projects using NVivo Server—this is the best solution for team work since everyone in your team can work on the same project at the same time. They can code, annotate and link source content and have immediate access to the changes made by other team members.
Work in copies of a standalone project and merge them into a master project at appropriate intervals—making use of user profiles to track changes.
While teams offer higher productivity and a richer perspective, they also present a number of management challenges. Early in a project it is important to determine the approach your team will take to:
Collecting and organizing data
Creating and cataloguing themes and topics (the node structure)
Whether you work with NVivo Server or collaborate in a standalone project you might want to consider the following:
Appoint a team leader who will keep the team on track and make final coding decisions.
Have regular team meetings to discuss interpretations, address issues and assign tasks—record the outcomes in a memo.
Have each team member keep a memo to record their progress, including any hunches, suggestions or questions—you could also do this in a single ‘teamwork journal’.
Early on, have multiple team members code the same collection of sources, then compare coding (using coding stripes or a Coding Comparison query)—this can help ensure a consistent approach.
To start with, make a node hierarchy for each team member. After team discussion, you can refine, merge and reorganize.
Aim for a clear node structure and use descriptions (in node properties) to make the purpose of a node clear for all team members.
To help team members understand the meaning of nodes, create a codebook that lists the nodes and their descriptions—refer to Export a codebook for more information.
As the project progresses, see which nodes have been created or modified and by which team member—do this in Node List View or by running a Node Summary report.
While a common node structure is important for efficiency and reliability— it should remain flexible so that new insights and exciting ideas are not lost.
If multiple researchers are coding the same data, you may be interested in the consistency of their coding. NVivo provides a number of ways to check consistency or coder reliability:
Run a Coding Comparison Query to determine the percentage of agreement and disagreement between coders.
Display coding stripes for users—you can open a data source and see the coding done by each researcher.
Filter the content of a node to see only the references coded by selected researchers
Remember that inconsistency in coding is not necessarily negative— it may prompt productive debate and deeper insights into the data.
