The Inverted Classroom

Mason, G., Shuman, T., & Cook, K. (2013). Comparing the effectiveness of an inverted classroom to a traditional classroom in an upper-division engineering course. IEEE Transactions on Education, 56(4), 430-435.

This study evaluates the effectiveness of the idea of the inverted classroom in upper-level engineering course. In the inverted classroom model, students spend their time outside of class reading, watching video lectures, and doing online tutorials, and in class, they work problems individually and in groups. The authors observed students in an engineering course, once in a traditional classroom (TC) setting and again in an inverted classroom (IC) setting. They found that in the IC, the professor covered more material, the students performed just as well on exams as they did in the TC, and the students adjusted to the class's format quickly. These results are promising for proponents of the inverted classroom in upper-level STEM courses.

Because I was raised by a teacher and am interested in education, I have heard a lot about the inverted classroom. However, all of my college courses have been traditional, with lecturers, chalkboards, and Power Point presentations. This doesn't surprise me because the discussion-based paradigm of the IC seems to suit liberal arts courses more than STEM courses. The authors do a good job of debunking this idea. An engineering education should prepare students for professional success, which requires self-motivated learning, a key aspect of the IC. If I ever become a teacher, I would consider selectively incorporating some IC ideas into small classes, especially the concept problem-based learning.

Twice, the researchers observed a control systems course for mechanical engineering seniors at Seattle University. The students in the two courses were very similar; there were 20 students in each class with similar GPAs and course credits. However, where the TC course followed the lecture-example-homework structure, the IC course involved YouTube lectures and in-class problem solving, both individually and in groups. The statistical methods used in the study are sound, and the authors break down the effects of the IC on content covered, student performance, and student perceptions. Additionally, the researchers do a good job of frequently referring to past studies that either align with or contradict the results of this study.

Based on the results of this study, the inverted classroom seems like a feasible paradigm for upper-level STEM courses. Teachers need to spend a lot of time putting materials together, especially video lectures, but they can reuse material from one semester to the next. Also, there are increasingly more online resources for various subjects, like Crash Course on Youtube, which has hundreds of videos about science, history, and literature. I believe that there is a future for technology in education, but we need to be selective about which technology we use in the classroom. Some educational software helps students learn without diluting the material, but technology in the classroom can increase student distraction and teacher frustration without much benefit. Another challenge is that many students rely heavily on traditional lectures in which the professor walks them through all of the course's material, but the inverted classroom requires self-motivation, which many students lack, particularly in lower-division courses. Despite these challenges, this study suggests that there is a place for many IC ideas in STEM courses, and incorporating these new ideas into education can encourage students to become lifelong self-motivated learners.

Reading these past few research articles from IEEE has given me a better understanding of the scientific method and science communication. I have learned that many cutting-edge developments in computer science, from security to education, are unknown to most people, however interesting or important they may be. Journal articles and scientific conferences are effective ways for scientists to communicate their research with one another. However, this isn't always the best way to inform the general population of the latest scientific discoveries. Phishing attacks and computational education are extremely important topics in today's technology-driven world, and I think that computer scientists have a responsibility to communicate their revolutionary ideas using means accessible to laymen. I hope that by taking this course in technical writing and presentation, I will become a more effective communicator, and if I become a computer scientist in the future, I will feel a sense of responsibility to help relate the latest scientific ideas to the media and to the public.

Computer science is ecsiting!

Goldberg, D. S., Grunwald, D., Lewis, C., Feld, J. A., & Hug, S. (2012). ITiCSE '12 Proceedings of the 17th ACM annual conference on Innovation and technology in computer science education: Engaging Computer Science in Traditional Education: The ECSITE Project. New York: ACM.

This article describes ECSITE, a program that helps incorporate computer science in the existing K-12 curriculum. In this program, ECSITE fellows pair with teachers and have weekly meetings to discuss how students can improve their understanding of their coursework through computation. These teachers are teaching anything from art or social studies to mathematics or biology. According to surveys from the students and teachers participating in ECSITE, the students learned about how computer science affects their everyday lives, and many teachers will continue to incorporate computation in their curricula. The authors conclude that their program is sustainable and will continue to proliferate.

Before I came to Purdue to study computer science and mathematics, I never learned about computer programming, algorithms, graph theory, or computer simulation. Luckily, I knew that as someone who enjoys logic, creativity, and problem solving, computer science would probably be a good fit for me. However, I believe that high schools should do a better job of introducing computer science to their students as a valid academic discipline as well as a career path. For me, through learning about computer science, I have become a clearer thinker, a better problem solver, and a more aware citizen of the universe, and I want to extend these benefits to more people in our society. I was drawn to this article because ECSITE, which is funded by the National Science Foundation, attempts to remedy the dearth of computer science education before college.

The ECSITE model seems simple yet effective. The fellows meet with K-12 teachers once a week to help prepare computer science material for their classes, and they also meet with faculty members at a university once a month to discuss what is and isn't going well. When the researchers obtained data about their program to elicit results and draw conclusions, they analyzed surveys of the ECSITE fellows and teachers. These surveys are purely qualitative information that could be subject to bias, and while they are definitely an effective means for learning about the status of ECSITE, I think that the researchers should also consider measuring more quantitative results, such as grades and college majors. This paper was published in an ACM journal, which is a reputable source for computer science information, and the authors cited sixteen sources, most of which were written in the past ten years.

According to surveyed teachers, the ECSITE students not only learned about computer science but also gained interest in computer science as a career path. The teachers themselves learned a lot about computer science and recognize the discipline as a valid academic field for their students. Because of ECSITE's success in the classroom, many ECSITE teachers informally recommend incorporating computation in the curriculum to their fellow teachers. Another advantage of ECSITE is that it incorporates computer science into the curriculum in an interdisciplinary manner, which prepares students to work across different fields, which I think is a key to our culture's success. Also, because ECSITE courses are not strictly computer science courses, students who may not have been interested in computer science found joy in learning about computation by taking a class that they would have already taken.

In my technical writing and presentation course, I need to write a persuasive essay and deliver a persuasive speech. One topic that I am strongly considering is computer science education, particularly in high school. This would be a valuable resource for me because ECSITE is an example of a successful program that integrates computer science into high school classes, which I think more high schools should be doing. In particular, I enjoyed reading about how specific classes learned about computer science as it relates to different fields. For example, AP Biology students solved puzzles and wrote algorithms to analyze DNA sequences. My opinion is that learning about computer science can aid high school students in becoming better problem solvers and in understanding subjects they are already interested in, such as math, physics, literature, and art.

Going Phishing

Mohebzada, J., El Zarka, A., Bhojani, A., & Darwish, A. (2012). Phishing in a university community: Two large scale phishing experiments. 2012 International Conference on Innovations in Information Technology (IIT), 249–254.

The goal of a phishing attack is to obtain someone's personal information online by pretending to be a trustworthy source. This study describes the design and results of two large-scale phishing attacks at a university. In the first attack, the researchers sent out a fake email from the IT department asking for people's passwords, and in the second attack, the researchers attempted to gain people's bank account information through a fake study. Of the 10,917 students, faculty, and staff members, 8.74% fell for the first phishing attack, and 2.05% fell for the second. The researchers did not notice a significant relationship between susceptibility to phishing attacks and other factors like age and gender, which contradicts previous research. This study reveals that many people are not aware of phishing attacks or the dangers of releasing personal information online.

Recently, I delivered a speech about the effectiveness of security images in preventing phishing attacks in online banking, and generally, computer security issues are interesting to me. As a millennial, I spend half of my life online, and I worry about the security of my personal information. Like most people, I probably repeat passwords too much from one website to another, and it concerns me that if someone just figures out my laptop's password, then that person will have immediate access to all of the saved passwords on my computer. I found this particular study relatable, since I attend a university and access my bank account online. I like to think that I would not fall prey to a phishing attack, but studies like this tell me that more people are vulnerable to phishing attacks than I might have thought.

In this study, the sample size of 10,917 is impressively large, although the sample only contains people at a university. If the researchers wanted to study the relationship between susceptibility to phishing attacks and, say, level of education, a more diverse sample would be necessary. The second section of the paper refers to several past studies of phishing attacks, which helps solidify the credibility of this study. However, I find the second phishing attack to be less than impressive, primarily because it makes no mention of what time of day the researchers carried out the attack. During this experiment, the IT department sent out a warning about the attack within two hours, but I wonder if the IT department would have been less responsive at a different time of day.

The article mentions that users are the "weakest link" of information security, which provides excellent justification for the study. Most computer security studies are purely technical, but we also need more research about how people's behavior impacts the security of their information. This paper concludes that many people are not only unaware of phishing attacks but also inattentive to warnings about phishing attacks. This implies that if an organization sends out warnings about potential attacks, damages might be limited but they will still exist. Interestingly to me, students in the study were more susceptible to phishing attacks than faculty or staff members, which could imply that more experienced users are more likely to be more cautious about releasing their personal information online. Overall, in order to learn more about how to minimize damages from threats to computer security, we need more research about how users behave in these kinds of situations.

I found this article using IEEE Xplore Digital Library, which is a reliable source of journal articles about computer science and engineering. I will continue to use this website to find more interesting articles to review. Because this paper refers to previous studies, I could put the research in context more easily. When I write introductions for research papers in the future, I will try to ease the readers into the new research by reviewing prominent old research. Additionally, I noticed that unlike the previous article that I blogged about, this article's abstract has keywords at the end, which also helps put the research in context.

Self-Presentation 2.0

Mehdizadeh, S. (2010). Self-presentation 2.0: Narcissism and self-esteem on Facebook. Cyberpsychology, Behavior, and Social Networking, 13(4), 357-364.

Mehdizadeh's research article combines the ideas of social networking, narcissism, self-esteem, and self-promotion. The paper analyzes data collected from one hundred students at York University through questionnaires and inspections of their Facebook profiles. The data reveals that more narcissism and less self-esteem corresponds to more Facebook activity and more self-promotion, and that males and females tend to promote themselves on Facebook using different techniques. In addition to encouraging further research, these results have implications in online marketing.

Over the summer, I removed the Facebook app from my iPhone out of fear that Facebook was hindering my productivity. However, during the few minutes I spend on Facebook per day, I check my notifications, read my messages, and scroll through my newsfeed. This is a glimpse of what my newsfeed looks like right now: a slew of news articles, some advertisements, a cute video of someone's kid, and a handful of status updates – some are witty observations, and others are life updates. Before I read Mehdizadeh's article, I spent a little bit of time thinking about how Facebook enables people to present themselves however they want to present themselves, and more than a few times, I rolled my eyes at what I considered parades of self-centeredness. Reading this article helps me understand the various means that people use to promote themselves on Facebook, including their pictures, personal information, notes, and status updates. Facebook grants people the opportunity to unleash themselves with little effort, so I find it unsurprising that narcissism and low self-esteem have a significant relationship with Facebook usage and self-promotion.

One strength of this research paper is the vast number of sources (eighteen of them), and I notice that most of the sources were published in the past decade. I also appreciate that the author did not claim that narcissism necessarily causes more Facebook activity, which is a common misconception about observational studies like this one. The paper mentions the limitations of the study, which include the subjective observation of the participants' Facebook pages. Additionally, I think that the scope of the study is weak, and future studies should include more participants at more universities. The three graphs look clean, and they help me visualize the differences between male and female behavior on Facebook, but the numbers on the graphs have little meaning to me. Generally, I like to see more graphs in research papers, especially with more objective measurements.

There is a dearth of research on identity production in "nonymous" (the opposite of anonymous) online environments like Facebook, and this study helps fill that void. One of the main purposes of this research article is to call for more research – more objectivity, larger sample sizes, and more settings – which I think has value on its own. Also, according to the paper, one concrete area that this study impacts is online marketing. Understanding relationships between self-esteem and Facebook usage is a goldmine for companies selling products meant to boost confidence. Overall, I think that this paper makes a significant point about the importance of understanding the connection between people's online and offline identities.

Reading this research article provides a good example of how a technical paper can be organized: abstract, introduction, methods, results, discussion, and conclusions. In technical papers that I write in the future, I will consider utilizing tables and graphs in the same way that this paper does. These visualizations make the key results easy to understand, and the text adds commentary that helps interpret these results.  In addition, this paper reveals the importance of conceding the limitations of studies. In this case, the subjectivity and small sample size in the study serve as motivations to expand on the results, and I hope that the technical papers that I write will also spur further research.

Color-Coded Direction Fields

My differential equations professor mentioned that for autonomous systems of differential equations, direction fields help us visualize the direction, but not the speed, of solutions. However, he also mentioned that we might be able to encode the speed of solutions using colors – red for fast and blue for slow – so I took it upon myself to do that. The MATLAB code for this is available on my github.

For example, consider the linear Romeo and Juliet system of differential equations described in a paper called The Lighter Side of Differential Equations, where $x$ is Romeo's love for Juliet and $y$ is Juliet's love for Romeo:

\[
\left\{
\begin{array}{l}
x' = -0.2y \\
y' = 0.8x \\
\end{array}
\right.
\]

The solutions seem to be cyclic. Near the origin, the solutions are close to blue because they move relatively slowly, and near the edges, the solutions are close to red because they move more quickly.

For another example, consider a simple nonlinear predator-prey model. Let's say that $x$ is the population of rabbits and $y$ is the population of foxes:

\[
\left\{
\begin{array}{l}
x' = x - xy \\
y' = -y + xy \\
\end{array}
\right.
\]

Here, all of the vectors in the first quadrant are blue because, relative to the vector at $(-2, -2)$, they have very small magnitudes. We can zoom in near the critical point $(1, 1)$ to get a better idea of the relative speeds of solutions near that point:

This looks remarkably similar to the direction field for the Romeo and Juliet system! Shall I compare the love of Romeo and Juliet to foxes eating rabbits on a summer's day?

How good am I at Flappy Bird?

Good news – I just played 100 games of Flappy Bird and recorded my scores.

Flappy Bird synopsis: Your job in this game is to navigate a bird through a series of gaps between pipes. If your bird manages to pass through 3 openings but then crashes into a pipe or falls to the ground, the game is over, and your final score is 3. The game is immensely difficult, despite the fact that the gameplay never speeds up and the gaps between the pipes never shrink.

I don't mean to brag, but I've wasted a lot of time on this game, so I'm pretty good. Here's what I've become curious about: Let's say I want to break my high score of 146. If I play one game, what are the odds that I surpass 146? How many games should I expect to play before I beat my high score?

To take a stab at this problem, I'm going to make a not-technically-correct-but-not-totally-unreasonable assumption: I can navigate my bird through each gap with some probability $p$, and whether my bird makes it through one gap has no bearing on whether he¹ survives the next gap (i.e., these events are independent). Once I know what $p$ is, I can find the probability that I get a score of, say, 3, by computing $p^{3}(1-p)$ (the probability that my bird survives the first three gaps but not the fourth).² In general:
\[P(\mbox{I get a score of } k) = p^{k}(1 - p)\]
In order to approximate $p$, I recorded my scores from 100 dreadful games of Flappy Bird. During those 100 games, I guided my bird through 2,551 gaps. That means that I encountered 2,651 gaps, 2,551 of them successfully, which gives me $p \approx 2551/2651 \approx 0.9623$. There's about a 96 percent chance that my bird will survive each gap – assuming, of course, that I didn't get significantly better or worse during those 100 games. Now, we have a simple model:
\[P(\mbox{I get a score of } k) = (0.9623)^{k}(1 - 0.9623)\]
Let's put this model to the test by plotting a histogram of my scores (the blue bars) along with the distribution of scores predicted by our model (the red circles):

I'm actually quite pleased with this – the distribution of my scores doesn't follow the model's predictions perfectly, but I wouldn't expect perfection since I only played 100 games. If I recorded 100 more scores (which isn't going to happen), the plot would probably look even smoother.

Recall the question at hand: How many games do I have to play before I beat my high score of 146? Using my value of $p$, we can predict the probability of achieving a score of at least 147:
\[P(\mbox{I get a score of at least } 147) = (0.9623)^{147} = 0.003509\]
This means that I should expect to exceed 146 points roughly 0.3509 percent³ of the time, or about once every 285 games. Conveniently, this also means that I should expect to play about 285 games before I beat my high score.⁴ Of course, 285 is just an expected value, and it could take me 2 games or 2,000 games for me to beat my high score.

Notice that even though the game never increases in difficultly (i.e., the hundredth gap is no more difficult than the fiftieth gap), getting a score of 100 is significantly more difficult than getting a score of 50. In fact, for me, getting a score of 100 is only $(0.9623)^{50} = 0.1462$ times as likely as getting a score of 50.

Rhett Allain of Wired Science wrote about this same topic from the perspective of someone who (no offense) isn't good at Flappy Bird. His article links to a blog post by Frank Noschese about gravity in Flappy Bird.

¹ Or she.
² This is a geometric distribution.
³ If you're playing along and you got 0.3521 percent, you're using the rounded value of 0.9623 where I'm using the not-as-rounded value of something like 0.9622783855149. This doesn't change anything significantly, and as you may have noticed, I'm not super focused on precision here.
⁴ The number of games it will take for me to beat my high score also follows a geometric distribution.