Artificial Intelligence

A web analysis on the debate around Artificial Intelligence

[aianalysis-blog]

Gephi Analysis of #VirtualReality

Network Analysis

Figure 5. Who Mentions Whom. 

Figure 6. Who Replies to Whom.

These two images were also from Netlytics, based on data I analyzed. It shows who mentions whom and who replies to whom on Twitter. In Figure 5, who mentions whom, you can see a lot of big nodes in the centre of the image and a lot of little nodes surrounding the big nodes. In Figure 6, who replies to whom, it clearly has a better structure of the connections between the people who reply to each other. In Figure 5 it’s all blended into each other, while in figure 6, all the nodes are far apart from each other.

Gephi

I used the network analyzing tool ‘Gephi’, to see for myself what the connections between the hashtags I used are. The hashtags I used were as mentioned before #education and #virtualreality. I analyzed data on Twitter, to get a better picture of how the Twitter accounts and their tweets are connected with each other. I analyzed this data through Gephi with three types of network graphics, these are based on who mentions whom. These graphics are In-Degree, Out-Degree, and Modularity Class.

Figure 7. Gephi, Out-degree 

Figure 8. Gephi, In-degree 

Figure 9. Gephi, Modularity

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Netlytic Analysis of #VirtualReality

The definition of virtual reality comes from the definitions from both ‘virtual’ and ‘reality’. The meaning of virtual is near and reality is what we experience as human beings. So, it basically means ‘’near-reality’’. To define virtual reality in technical terms, it’s a three-dimensional computer-generated environment which can be explored and interacted with by a person or multiple people.

Now, a combination of virtual reality and artificial intelligence (intelligence demonstrated by machines) will unlock a future with safe and competent intelligent machines, able to learn exponentially through self-training and intelligent, realistic simulations. You can begin to see how the mingling of academic people and technologies might combine Virtual Reality and Machine Learning to create a force more disruptive than either alone.

In order to understand how people, respond to a technological future within the education, I analyzed two hashtags #virtualreality and #education on Twitter by using the data analyzing tools called Netlytics and Gephi. Because Twitter is a platform that uses a lot of hashtags when posting something, I chose for Twitter to analyze data about these two hashtags.

Text Analysis

I did the text analysis of these two hashtags, using Netlytics as mentioned before. You can find all the graphs and charts down below with explanation added beneath it.

Figure 1. Word Cloud.

The Word Cloud shows that #virtualreality is the biggest popular topic on Twitter. #virtualreality has been mentioned 1584 times in people their tweets. The three most popular hashtags are: - #virtualreality (frequency: 1584), - #VR (frequency: 1096), - and #education (frequency: 597).

This shows that virtual reality is quite related to the topic of education and artificial intelligence since it happens more and more in the classrooms that the teachers work with virtual reality to educate the students.

Figure 2. Words over Time.

This visual shows the Words over Time, the two biggest bars in the graphic is as you can see: Virtual Reality (purple bar) and Education (mint-y bar). There are both quite equal in size. These two were the most used words over time. If you look at the time part, it these two hashtags has been used constantly. Where education lowers in use, virtual reality is still being used a lot. After these two, the hashtags #technology is the 3rd biggest word that has been used quite a lot In between too.

Figure 3. Manual Categories.

This image shows that the tweets that include the hashtags #education and #virtualreality mostly belong to which category. As you can see, the biggest category is ‘’feelings (good)’’. This means that the tweets about #education and #virtualreality combined mostly belong to the category of ‘’good feelings’’ which is 80% of all the categories. The second and third best categories are Time (43) and Appearance (24).

Figure 4. Top Ten Posters Mentioned in Messages.

According to the Report in the Netlytic tool, it shows that ‘kuriharan’ is on top of the top 10 Posters mentioned in messages (#mentions: 20). Following with discoverhaptics (#mentions: 9), allybarrow (#mentions: 9), silviadellai1 (#mentions: 6), gfxcoach (#mentions: 6), beatsaber (#mentions: 6), certesnews (#mentions: 6), sgh_rts (#mentions: 6), technative (#mentions: 6), and haroldsinnott (#mentions: 6).

The user who was mentioned the most, Kuriharan, is the President of Tokyo Chapter at Government Blockchain Association in Tokyo, Japan. The second user, Discoverhaptics, is a twitter profile of a company who is all about haptic technology. They share their passion for haptics and a whole lot of information about haptics. This shows that the organization is quite in the virtual reality world and into making the interaction between a human being and a machine more ‘’humane’’. Which also has a BIG influence on the combination of virtual reality and education.

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Gephi Analysis of #IOT

Network analysis

Figure 1.7 Gephi, modularity class 

Figure 1.8 Gephi, Out-degree class 

Figure 1.9 In-degree class

When I put the layout within the graph together with strong gravity, the two biggest nodes within and out degree became way more clear. Userexperienceu and rww are the two biggest nodes. Userexperienceu is Karl Smith that has loads of knowledge about currencies and AI and want to show this knowledge on his twitter with a lot of explanations how things should be done, he set up four currencies and he is the inventor of open network eco-system protocol which delivers smart-living ecosystems for houses. Rww is an information website about the internet of things and the latest news on it and interesting topics such as fintech and blockchain. As you can see in the in-degree class (which means measuring who gets listened to the most) They are the biggest blue dots and the network is very dense and connected with each other. In the out-degree graph (where you can measure who listens the most), you can see the yellow small dots but again the two big dots so it means that they also actually listen to each other a lot.

[aianalysis-blog]

Netlytic Analysis of #IOT

The internet of things is the concept of basically connecting any device with an on and off switch to the Internet (and/or to each other). This includes almost everything that is electric. So basically, the IoT is a giant network of connected “things” (which also includes people) (Morgan, 2014). In this research, I’m planning to connect the famous internet of things to education because in this age every educational institution uses the internet of things and I wanted to see how they correlate with each other.

In addition to this, I did several analysis’ on Twitter and Instagram to see how people respond with using the Internet of things within the sector of education. On hashtagify.me, results show that #iot was only used 79 times on Instagram and for Twitter, #internetofthings and #iot was used 735 and 762 times. As a result, I made the decision that Twitter is most suitable as there was way more conversation on that platform. Also, I first did a text analysis without education and only with the hashtag internet of things. After that, I included the hashtag education and the results were quite interesting.

Text Analysis

Figure 1.1. Word cloud Netlytics

I started the text analysis with using the tool Netlytics and creating a word cloud within it. The word cloud shows that the hashtags internetofthings (735) and iot (762) are a very popular topic within Twitter. It’s very closely related to our main topic, namely by the way the hashtags ai (233) and artificial intelligence (205) are mentioned loads of times connected to my particular hashtags. #bigdata (242), #blockchain (196), #machinelearning (164) and #socialinfluence (150) were also mentioned a lot on the platform and this fits well towards our topic. The one thing that was interesting to me were the hashtags #smartcity (66) and #businessintelligence (64) as these are also related in a way to education as the internet can make you smarter and help you with business or making smart cities. It is interesting that these things were mentioned on Twitter. This whole graph indicates that people are interested in the internet of things and the technology that comes with it such as AI, deep learning, block chains etc.

Figure 1.2 Top 10 mentioned hashtags in wordcloud

Here are the top 10 most mentioned hashtags related to #internetofthings. What I found interesting about this chart is that #socialinfluence is included. I think this is because on the internet there are a lot of influencial things right now. Can be people, but also chatbots and other AI related things. And this part includes again; education. With the internet of things there are loads of people being influenced, some in a good way and some in bad ways. But in the end we learn something out of it instead.

I was interested how related education actually is to internet of things and therefore I did another dataset on netlytics but included the #education into it. Then, I did the wordcloud again, and here is the result:

Figure 1.3 Wordcloud with #education included

From this wordcloud we can see that there are a lot of different hashtags therefore interesting ones such as #connectingpeople (5), #healthcare (8) and #topinfluence (10). On the other hand, the wordcloud is way smaller but education and internet of things are quite related as they have 27 of the same tweets mentioned.

Figure 1.4 Words over time

In this graph, you can see that in the word cloud the most important words were the internet of things, iot and education. Also, the word “tech” is a word that has been used a lot in relation to the internet of things. Together with a lot of Twitter users, it had a high peak during the 17th of May. It is interesting that around that time many more people were suddenly intrigued by the word technology. On the other side, #internetofthings and #education are very consistent during the analysis, they are both interesting at the same level, which indicates that people were interested in both of the hashtags during this time.

[aianalysis-blog]

Gephi Analysis of #DeepLearning

NETWORK ANALYSIS

Figure 1.5 shows the visualized connections about “who mentions whom”, where as figure 1.5.1 is a close up of the middle nods. To make this a bit clearer, I used Gephi.

 Figure 1.5 Who Mentions Whom

Figure 1.5.1 Who Mentions Whom Close Up 

Figure 1.6.1 #deeplearning [who mentions who indegree via Netlytic]

Figure 1.6.2 #deeplearning [who mentions who modularity via Netlytic] 

Figure 1.6.3 #deeplearning [who mentions who outdegree via Netlytic] In the above figures [1.6.1, 1.6.2, 1.6.3], we see a lot of users who mentions other users, for example by retweeting, whilst using the hashtag #deeplearning. This Gehpi graph has 554 nodes and 6340 egdes, which makes it a large data set. Some users with the largest connections are; @tsundu_mak, @arkangelscrap, @mikequindazzi, @evankirstel and @ronald_vanloon.

Figure 1.7 Who Replies To Whom

Figure 1.7.2 #deeplearning [who replies to whom indegree via Netlytic] 

Figure 1.7.3 #deeplearning [who replies to whom outdegree via Netlytic]

Figure 1.7.4 #deeplearning [who replies o whom modulartity via Netlytic]

In the above figures, we see the connections about who replies tot whom whilst using the hashtag #deeplearning. In this Gephi graph there are 20 nods and 15 edges, so it is not as big as the who mentions whom graph. But again, there are a few users which stand out more. These are; @tsundu_mak, @m_learningnews, @machinelearnonl, @dlandmlapp, @machinelearninz and @analyticbrigde.

[aianalysis-blog]

Netlytic Analysis of #DeepLearning

TEXT ANALYSIS

In figure 1.1 we see the most popular hashtags used in combination with #deeplearning, which is the hashtag with the largest data [1011]. The second largest hashtag is #machinelearning [798], followed by #ai [856], #bigdata [622], #iot [464], #nlp [284], #artificialintelligence [283] and #robots [281].

WORDCLOUD

Figure 1.1

Figure 1.2

WORDS OVER TIME

Figure 1.3 shows the use of the earlier mentioned hashtags over a period of time. As we can see, #deeplearning is together with #machinelearning the most popular hashtag in comparison to other hashtags (or usernames) at that time. The hashtag #deeplearning seems to have two slightly lows, right after two peak points, but has without a doubt the largest data set. Looking at posts connected to these words, I tried to find something related to either education or finance. I came across the posts you can see below [figure 1.3.1]. This post by @JohnTweepy mentions the hashtags #deeplearning, but also #fintech and #insurtech

Figure 1.3 Words over time Figure 1.3.1 Post [Tweet] by @JohnTweepy

CATEGORIES When you take a look at figure 1.4, it is obvious that size [81] and shape [72] are the two largest categories. Adjectives used in the size category are; big [65], massive [9], small [3], short [3] and huge [1]. In the shape category, words that are being mentioned are; deep [52], high [17] and straight [3]. Another category which came up quite often is appearance [36], with the adjectives light [25], dark [8] and long [3]. Moreover, feelings (good) [33], the words that were mentioned here, are nice [13], great [11], proud [3], kind [2], happy [2] and good [2]. Lastly, we have the category quantity [29], with only two adjectives; light [25] and many [4].

Figure 1.4 Categories

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Gephi Analysis of #FinTech

By using Gephi as a tool of network analysis, I could certify the important people or organizations in this network and clearer connections between each twitter account. I looked for three types of network graphs;in-degree, out-degree and modularity.

Figure8. In-degree. Who mentions whom (Gephi)

In-degree measures the influence of accounts and it shows which users are the most mentioned accounts. This graph has several large nodes with many smaller surrounding nodes. In this network, three nodes with red colour;Fisher85m, evankirstel, mikequindazzi are the most influencial users with the #fintech in twitter. First, Fisher85m is the account that belongs to a person named Michael Fisher, an Analyst and Technology Evangelist - a person who inspires and convert businesses and individuals through interaction, content creation and communication about a technology. Evankirstel also belongs to a person, Evan Kirstel, a social media influencer in IoT, Data Security. Lastly, mikequindazzi is an account of Mike Quindazzi, a Managing Consultant at PwC.

Figure9. Out-degree. Who mentions whom (Gephi)

Out-degree centrality shows measurement of sociality. Therefore, by analyzing this graph, I could check who is the most active(communicative) actor in the discussions about #fintech on Twitter. Azurflorian, a company called “IPFC online” which focuses on Digital Marketing and E-Business, and bdsharmas, Bharat D. Sharma, an expert in Cybersecurity and fintech are the users that seem to highly reach out to others.

Figure10. Modularity. Who mentions whom (Gephi)

Modularity class describes how the one big network is divided into sub-networks. This graph shows what accounts are more densely connected to each other with the topic of Fintech on Twitter. This graph is a result of 86 communities (accounts). In this graph, there are four obvious groups of accounts that are connected to each other. First, Blue coloured group of nodes includes fisher85m, evankirstel, jblefevre60, antgrasso. By analyzing the main nodes of the blue group, I found out that the accounts in blue group have all belonged to an individual who are actively working at the finance and technology sectors. To be specific, antgrasso belongs to Antonio Grasso, a influencer and a speaker in the field of AI, blockchain and fintech. Second, nodes with red colour have mikequindazzi, azurflorian, enricomolinari, bdsharmas, 2vero as their main nodes. All these accounts were also belong to an individual except azurflorian. The group of nodes with pink colour was interesting because all of the nodes had a character of having a large number of followers and following. For example, ralexjimenez, which has 8810 followers and 665 following, is a twitter account of Fintech Strategist named Alex Jimenez and it is a verified account which indicates that the account of public interest is authentic. Syscoin, which is one of the most interesting nodes in pink group with 69818 followers and 5602 following, is a business on the cryptocurrency based on blockchain. By looking at those graphs through Gephi and Netlytics, I could find the answer of our sub-questions; What kinds of actors are involved in the debates on AI online? Based on the analysis that I did, most of the accounts with a power turn out to be the accounts that belong to individuals whose occupation is closely related to the finance or to the technology industry. Among them, there were lots of speakers who are actively giving presentations about the fintech sectors such as Michael Fisher, Evan Kirstel and Antonio Grasso. Since most active accounts are all experts in a technology field, who wants to tell the promising future of AI and Fintech, there were not enough messages about the risks of technology. Therefore, after I finished network analysis, the reason why there were only ‘Good’ comments in ‘Feeling’ category in text analysis (figure 5) became clear.

[aianalysis-blog]

Netlytic Analysis of #FinTech

FinTech is a combination of the words ‘Finance’ and ‘Technology’. Artificial Intelligence(AI) is now actively being used in this fintech field. Recently, AI has helped the technological advancements in the financial industry, such as using facial recognition to log in to financial apps and use voice commands to check their balances. (Forbes,2018) Moreover, according to the Finextra, AI is going to make a game-changing impact in the fintech industry. However, there are various opinions on the future of AI in Fintech. An article from the Forbes claims that people need to be aware of the risks of using AI technology in the financial sector. It says that increasing reliance on AI comes with a reduction in jobs.(Forbes,2017) On the other hand, the author of an article of finextra strongly argues that “With massive funding and intense competition, AI is the only way Fintech companies can stay ahead in the race in 2018”.

In order to understand more about who are interested in this field and how people respond to using AI in the financial sector, I analyzed #fintech on Twitter by using Netlytics and Gephi. As we can see in the hashtagify.me, Twitter and Instagram were the platforms that have a high popularity of #fintech (71.6 out of 100) and #AI(74 out of 100). Among those two platforms, Twitter was judged to be a more appropriate platform for analyzing because there were most active posts related to #fintech. I also gathered data from Instagram through Netlytics, but there were no meaningful topics that are closely related to AI and Fintech.

Text Analysis I did the text analysis of #fintech by using the tool called Netlytics. First of all, I looked up the words cloud.

Figure 1. Word cloud: The Top 100 most popular hashtags_netlytics

The word cloud shows that fintech is one of the popular topics on Twitter. #fintech was mentioned 996 times on tweets. The frequency of #ai is 307 and this indicates that fintech is a topic that is related enough to artificial intelligence, our main topic. #blockchain (516) #bitcoin (173) #crypto (208) and #cryptocurrency (152) were also a lot used in twitter. This represents that people or organizations who use twitter nowadays are interested in the field of cryptocurrencies such as bitcoin among many other sectors of fintech. Following graph presents the top 10 hashtags related to the #fintech.

Figure 2. Top 10 Hastags with high frequency by using Excel

I found it interesting to have a #healthtec in word cloud as a related hashtags. It has less frequency (40) compare to top 10 hashtags but still it was interesting because it seems to have no close relationship with #fintech at all. Therefore I looked up specific tweet related to #healthtech.

By closer looking at one specific tweet, I could find out that people often tweets related articles from medium with the keyword. The reason became clear why #healthtech, which seem to have no big relationship with #fintech, can be found in the word cloud.

Figure 3. Words over time

The thickest purple bar of #fintech in figure 3 shows that #fintech has been the most used word over time. Although #bitcoin has been also used a lot, overall, it is clear that fintech has been used constantly the most.

Figure 4. Top 10 Posters Mentioned in Messages (Netlytics)

According to the Netlytic Report, Top 10 Posters mentioned in messages are ipfcoline1 (#mentions: 70), andi_staub (#mentions:36), thomaspower (#mentions:27), syscoin (#mentions: 16), fintechna (#mentions: 16), financialbrand (#mentions: 13), motorcycletwitt (#mentions: 10), therudingroup (#mentions: 9), platinumcpromo (#mentions: 7), enricomolinari (#mentions: 6). Ipfcoline1, the user who was most mentioned in messages is an Info-Plus Formation Center, a computer consultant in France. Among 10 posters, 5 accounts are experts (andi_staub, thomaspower, motorcycletwitt, therudingroup, enricomolinari), 3 accounts are organizations (ipfcoline1, syscoin, platinumepromo) and 2 accounts are media. This represents that the experts and organizations that are related to the computer and financial fields are interested in #fintech.

Figure 5. Categories (Netlytics) This image represents that the most of the tweets that have #fintech belong to the category called ‘feelings (good)’. There are only ‘good’ feelings and words such as ‘great’(45), ‘good’(12), ‘proud’(12) are included. Network Analysis Netlytics  

Figure 6. Who Mentions whom (Netlytics)

Figure 7.Who replies to whom (Netlytics) The networks image in Netlytics shows ‘Who mentions whom’ and ‘Who replies to whom’ in twitter. In the graph of ‘Who mentions whom’, there are a few big nodes in the center of the image which have connections with lots of other smaller nodes. Network graph in Netlytics shows that the “who mentions whom” graph is dense, while who replies to whom is not. In the network analysis in Gephi, it shows clearer structure of connections between accounts.

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Literate Review of #IOT

Interaction system based on Internet of Things as Support for Education

The article is about the new paradigm (internet of things) that is revolutionizing computing. It is focused on the education field, where the Internet of Things can be used to create more significant learning spaces. They propose a system that allows students to interact with physical surrounding objects which are virtually associated with a subject of learning.

They conducted an experimental validation of their approach with rewarding evidence that the model that they made improves student’s learning outcomes. The paper also proposes a system that allows students to interact with a set of physical objects in the surrounding. These objects each has associated one (or more) virtual object which provides information that allows the student to reach a learning achievement, as to how they work, how it can be used etc. This content is what they want to add to the internet of things. They also used focus groups. The students were divided into two independent groups, control and experimental. The experimental group had access to the internet of things whereas the control group received traditional lectures.

Nowadays, people have access to learning content and collaborative learning environments supported by computers anytime and anywhere. The purpose is to allow the students to manipulate the objects (both physically and virtually) in order to increase their understanding of the issue. The objective is to contribute and to enhance teaching and learning through the internet of things.

The ubiquitous learning also allows the right combination of virtual and physical spaces. The purpose of ubiquitous computing technology is basically improving learning processes. It is trying to adapt learning resources to different contexts of use of apprentices. The system that they’re talking about can be used in several academic programs where it is necessary the interaction of the students with the objects around them, varying from health studies where students can learn about some devices used in the clinical practice to mechanical engineering studies where it is necessary to understand the inner workings of an engine, for example. From the focus groups, the experience showed that students who had access to the internet of things improved their academic results. Also, according to Wang, internet of things has characteristics such as motivation, playfulness and allows teachers to teach students according to their aptitude. Teachers can choose basic materials to suit students. Students can also learn at their own pace according to their capabilities so no one gets left out.

Within this area, the internet of things plays an important role in learning processes in formal and informal education. In general terms, the internet of things has been proved to be a fun tool that allows students to learn in a better way. The obtained results showed evidence that the internet of things, applied as a tool to support the teaching process, improves student academic performance. Furthermore, using real objects and associate them with a learning resource facilitates meaningful learning, as it allows to link specific knowledge to a real context.

[aianalysis-blog]

Literate Review of #Deep Learning

‘Deep learning’ — the hot topic in AI

How do we integrate AI in our society to help improve the education of increase financial profits? To answer that question, we must look at different factors of AI. Deep learning is one of the many techniques AI uses to create a pattern where a technological device can provide better service to its user. Water (2018) states that this may be one of the most overhyped technologies, but that there is good chance that it will one day become the secret sauce in many different business processes.

The term “deep learning” refers to the use of artificial neural networks to carry out a form of advanced pattern recognition. Algorithms are trained on large amounts of data, then applied to fresh data that is to be analysed. It has become the hottest subject in the field of artificial intelligence, thanks in particular to breakthroughs in image and language recognition in recent years that have approached or surpassed human levels of comprehension. (Water, 2018)

But how do we apply deep learning? Water (2018); ‘’The best way to think of deep learning is as a form of advanced analytics. Given enough data to train the algorithm, it can be used in many different tasks. The challenges include identifying the types of problem that are most susceptible to being solved with this technique, picking the particular approach that is best in any given situation and making sure the algorithms are fed with a good supply of high-quality and timely data.

[aianalysis-blog]

Literature Review of #FinTech

On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption, and Transformation in Financial

In order to answer our research question and study more about fintech that the graphs cannot tell, I searched an article about the future of using technology in finance sector. This article explains the benefits and risks of technology in the financial industry by using lots of case studies; Atom Bank, IndieGoGo, PayPal, ING, Europe-based Deposit Solutions and so on.

The authors claim that there are several benefits of using technology in the financial industry. They say that the AI and robotics will continue to achieve a critical role in financial services. Moreover, they argue that technologies such as AI and blockchain lead to the higher functionality and new efficiencies in cross-border payments. It can increase settlement efficiency, shorten settlement times, and reduce reconciliation effort. (Gomber, Kauffman, Parker, & Weber, 2018) The new services in fintech such as robo-advisors and social trading platforms based on technology can provide customers with more ease of service usage since customers do not have to handle a multitude of providers with traditional services. Furthermore, the authors explain the positive aspects of fintech by doing the case study of ‘Atom Bank’, a UK mobile-only bank. They say that by using fintech deposit services, Atom provides customers a convenient way of managing their money.

On the other hand, the authors also mention the risks of using technology in finance sector. The article argues that technology may also lead to new types of risk or exacerbate existing ones. For instance, the robo-advisory services based on AI are not designed to consider the more personal aspects of financial investments like real estate or individual tax situations. Therefore, robo-advisory services can generate risks in customers’ specific situations. Furthermore, the authors claim that the multiple problems in the scalability and IT security have yet to be solved.

Overall, this article emphasizes that it is important to assess and oversee the innovations, disruptions and transformations in fintech on a repeated basis going forward because emerging technologies, new processes and customer-friendly services by using fintech seem to be undergoing continuous development. (Gomber, et al., 2018)

By doing the literature review of this article, I could find out the answer to our research question; Technology such as AI becomes more influential in financial sector nowadays, and it can affect human in either a positive or negative way in financial industry. Therefore, it is important to manage negative aspects of AI for the promising future.  

[aianalysis-blog]

Literature Review of #VirtualReality

On the Usability and Likeability of Virtual Reality Games for Education: The Case of VR-ENGAGE

The popularity of artificial intelligence and virtual reality in combination with software games has been increasing a lot among children and teenagers. Many empirical studies confirm that software games constitute the principal way that children and teenagers/youngsters become acquainted with the use of computers and devote a lot of their time on them (Mumtaz, 2001).

Most people may think that gaming will be a bad influence on students’ grades and educational behaviour. What people actually don’t know is that there is something like educational software games which aim at increasing the motivation and engagement of students while they’re learning. Likeability is one of the key issues that games with educational content are struggling with. Games tend to have very complex user interfaces to become more attractive, but if they acquire educational content, they may lose their attractiveness because commercial games have more of an appeal to people.

However, most of these games are created with entertainment as their main goal, they don’t aim at creating educational games for the users. But there are still technologists who try to create software that will motivate and will engage students in learning. It has shown that if you are actively engaged in a learning activity it’s beneficial for learning. Hybrid applications that combine education and entertainment are often called ‘’edutainment’’.

But before we bring software games that are educational for the students in the classrooms, there are a few issues that need to be checked first. The most important thing is that the games need to be usable and likeable by the majority of the students. The likeability is mostly affected by usability: this means that the less usable a game is, the less likeable it will be for users. If there are students who don’t like these games and find it difficult to interact with games, then instead of helping these students in their learning process, it will do the opposite and it might cause problems to their results.

Besides this, there are also other issues that affect the likeability of educational software games. The unavoidable competition of educational games with commercial games is one of these issues. It affects the tastes of the users, especially if they have a lot of experience with commercial games already. There are already some criticisms from researchers concerning the quality of the existing educational games, for example, Brody (1993) pointed out that there are produced some not-very-educational games and some not very-entertaining learning activities. Having this said, educational games should be examined before educational games become an option in classrooms.

There is a game, called VR-Engage, and the purpose of this game is to teach children geography through a virtual reality adventure game. The conclusion is that the usability and likeability aspects of an educational game are the key points to look out for when proposing it to classrooms because we don’t want to make the students’ learning process more difficult than it already is.

[aianalysis-blog]

Gephi Analysis of #MachineLearning

Graph 1: Modularity Class The first graph shows the modularity class graph. You can very clearly see that all the nodes are divided into groups, which means that these accounts mostly communicate in their circle of people. There’s not a clear node that’s much larger than the others, but the group of green nodes shows bigger than the others, which means that these accounts are more active than others.

Graph 2: In-degree The second graph shows the in-degree. You can tell that user mikequidazzi, and fisher85m are the most influential accounts in the tag. You can tell that this graph is quite dense, which means that there aren’t a lot of influencers in the debate. However, the jumping out nodes for users mikequidazzi and fisher85M make sense, as these are professionals in the AI field.

Graph 3: Out-degree This graph shows the most active communicators in the tag. Account machinelearn_d is the most active communicator, together with habitline. This makes sense as the machinelearn_d account is an actual account about all things machine learning. The graph is very dense with little nodes that jump out, so it is to conclude that machine learning does get talked about, but it’s not an active tag with a lot of daily communicators.

[aianalysis-blog]

Netlytic Analysis of #MachineLearning

For the following analysis, multiple tools were used to investigate the hashtag machinelearning. In this, the reader will get an insight of the term machine learning.

Figure 1: Word Cloud of #Machinelearning The following data collection was conducted on May 23, 2018. To conduct a textual analysis of the hashtag machinelearning on Twitter, the tool Netlytics was used. First, a word cloud was created which shows the top 50 words used concerning machine learning. The word cloud shows that machine learning is a quite popular topic on Twitter, as it’s mentioned 576 times, alongside with, #AI (514 times), #bigdata (213 times), and #iot (252 times).

Top 10 Most Frequent Used Words To get a clearer view of which words are most frequently used in combination with machine learning, the bar chart below shows the top 10 most frequently used words in combination with the hashtag.

Figure 2: Top 10 Most Frequent Used Words in relation to #Machinelearning Top 10 Posters The following pie chart will give an overview of the top 10 posters of the hashtag machinelearning. This pie chart shows that the number 1 user of the hashtag takes up most of the topic, with 31,5% just for the one account. This account is @machinelearn_d, and after follow up research, it makes sense as this is a twitter account dedicated to machine learning. The other posters in the 69,5% are almost evenly divided over the other 9 posters of the top 10. 

Figure 3: Top 10 posters of #Machinelearning

[aianalysis-blog]

Literature Review of #MachineLearning

The e-puck, a Robot Designed for Education in Engineering  The article discusses and introduces a new robot that’s designed for education in engineering. It goes in depth on the design of the robot, how it’s built, and what it does. Moreover, it focuses on what robots were used before the e-puck and how it is used in education. Using robots in the engineering education isn’t something new. The article goes through previous robots used in the education field and what makes a good educational robot. The EPFL, a school in Switzerland, has started experiments with the e-puck. They organized excircles in signal processing, automatic control, behavior-based robotics, distributed intelligent systems, and position estimation and path finding of a mobile robot. The e-puck regularly gets evaluated by students. The goal of the courses provided by the robot Is the understanding and practice of embedded programming. On the question ‘The e-puck robot is a good tool to illustrate the concept of the course’ most students have been answering ‘totally agree’ since 2005. More than 200 students over 3 years have validated the concept of the e-puck and shown their satisfaction using the robot. The open-source nature of the robot improves the quality of the support to the students by providing full access to knowledge at every level. Mondada, F. (2009) The e-puck, a Robot Designed for Education in Engineering. Retrieved from: https://infoscience.epfl.ch/record/135236/files/epuck-robotica2009.pdf