IoT has been a buzz word for some time now and is being talked about in applying to all and sundry fields. But nowhere is there talk of applying it in cognitive science and web proctoring.
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Cognitive science is all about measuring the effectiveness of a human in his cognitive endeavors. What we mean by cognition is related to how a human is learning, understanding and keeping new knowledge in memory.
In massive open online courses (MOOC), students learn and take exams online. At present there is no way to properly proctor those online exams to be confident that the education imparted online is effective. Due to difficulties in effectively proctoring these online exams, there is a widely held perception that classroom-based education is somewhat more effective and desirable than online education. To change this perception, the effectiveness of proctoring has to be improved — this is where IoT comes into play.
I will explain how IoT can be an effective tool in cognitive science research and how IoT can help effectively proctor online courses in such a way to impart the badly needed credibility for online education.
Cognitive science and IoT
In cognitive science research, various sensors are fitted onto the subject and physiological parameters are measured and recorded. These parameters might be brainwaves measured through EEG or heartbeats, pulse rates, iris contraction, skin conductivity, etc. These parameters, when studied with other parameters which are related to how a person understood a certain topic or how the subject retained certain topic through a questionnaire, provide valuable insights. The sensors and sensor data are the subject of IoT wearables which can measure and send data through a wireless infrastructure to the cloud. If IoT technologies are adapted to the research of cognitive science, the data from various geographically different places can be collected, stored and studied with the help of the IoT cloud. Researchers will have valuable data for the asking and will actually “open source” the data to multiple stakeholders to conduct research effectively. At present, there is no such infrastructure which collects, stores and analyzes data from cognitive science related research.
A representative sensor pad for measuring EEG is shown below.
Unfortunately, the data from the headset stays with the nearest receptor and is stored in a local server. If the data is put into the IoT pipeline towards an IoT cloud, the data can be leveraged and properly “open sourced” for the greater good.
Where IoT infrastructure fits in is where data collection and dissemination has crossed certain limits. The headset shown above for EEG-based cognitive science research can be purchased off the shelf and used by anybody. This means with the availability of cheap off-the-shelf sensor pods, people who are interested in quantifying themselves will start using them. It would be a pity if no effort is made to collect the data, store and analyze it in a central cloud-based structure (which is nothing but an IoT cloud).
Where IoT fits in web proctoring and MOOC
Another interesting scenario where the explosion of data occurs but is somehow ignored is web proctoring and massive open online courses. MOOC somehow lacks credibility when it comes to companies and employers accepting the certificates to be as credible as offline standard classroom course certificates. It is difficult to imagine a MOOC course conducted by Stanford to be as respected as the same course conducted in a classroom in Stanford. It all boils down to whether a student who took the course has sat for the exams in a manner in which the exams were properly proctored. In other words, it is difficult to proctor an online exam because the student is invisible and there are many ways to cheat in an online exam when compared to an offline one.
Sure, there are platforms where students are monitored by online proctors through web cameras. Here a number of students’ webcams are monitored by online proctors, but there are no tools to capture other parameters of students who are taking the online tests. It is easy to cheat in an online exam just by keeping another screen somewhere in the room to get answers to the online questions. True, there are 360 degree cameras, but there is no data to validate that these are effective in curbing online cheating.
To end this menace and to increase the credibility of online courses and exams, a number of measures has to be taken. The most important one is to collect other kinds of data from the student along with webcam data, such as EEG, ECG, skin resistance and run analytics. One example is how the student reacts when he came across a questions which he feel is difficult. This can be achieved by carefully monitoring the above said sensors, namely EEG, ECG and skin resistance. Based on their inputs, the webcam data can be closely examined to see whether the student is trying to cheat or has already cheated, if the webcam data has been recorded.
Interestingly, only the webcam data is enough to measure the heartrate, and this data will provide a certain amount of input by way of whether the pulse is increasing or decreasing while a student attempts to answer a question.
The whole gamut of online exams and courses can be brought under the IoT umbrella and all analytics can be brought to IoT cloud, stored and analyzed for a more effective MOOC and online certification. Eventually more data can be brought in with the help of sensors available in any modern laptops namely mics for voice recognition, fingerprint sensors for identity management, facial recognition, etc.
So where does it all lead to?
Based on the details about how IoT can be leveraged for both cognitive science and MOOC, a question may come about why cognitive science and MOOC are banded together in this article. The reason is obvious: Both fields are measuring physiological data in one way or another and can immensely benefit from IoT infrastructure and practices.
Therefore IoT can and most probably will help many fields mature and become widely available to the masses. This article highlighted just two fields which are poles apart but suited for IoT to take over because of commonality of data from physiological sensors. Their applications might be different, but the underlying platform can be same, to benefit all.
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