#LAK12 Is LA strictly behaviorist?

I thank Bianka Hadju for calling my attention to this statement about behaviorism in Siemens' and Long's Penetrating the Fog piece:

Since we risk a return to behaviorism as a learning theory if we confine analytics to behavioral data, how can we account for more than behavioral data?

Behaviorism is a psychological and educational theory which, as alluded to in the statement, is no longer in favor in most educational circles. The primary criticism of behaviorism in plain terms is that this educational theory would simply explain differences between learners by observing and measuring their outward behaviors. Behaviorism does not account at all for inner mental states, thoughts, feelings, cognition, meanings ascribed to events by learners, etc. If there is no difference in behavior, then there is no difference between the learners. A fascinating explanation of the theory and it's criticisms is available at the online Stanford Encyclopedia of Philosophy.

This ties back to Ryan S.J.d. Baker's presentation last week about model building in educational data mining. He described an online reading tutor system which was tested with U.S. students and students in the Phillipines. Baker used the system to build and test his analytic model of "gaming the system." He could tell who was gaming the system by observing behaviors such as the click patterns, order of clicks, wait time, etc. What was even more interesting to me, though, was his finding that although both groups of students gamed the system, the meanings behind this observable action --their feelings about the activity, their attitudes toward the use of the system -- were vastly different in each cultural group. The students' feelings and attitudes, which are hugely important to the educational process, cannot be measured by clicks at all. The two groups displayed the same behavior, but their reasons for doing so were very different.

Will we focus on measurable behaviors without considering the other important changes we wish to inculcate as educators, such as attitudinal and affective changes?

Looking forward to hearing about Purdue's "Signals" project today!

#LAK12 Model-building in EDM

Ryan Baker's presentation on educational data mining today as part of the Learning Analytics and Knowledge MOOC helped me to better understand the model development concept in EDM. One of his slides listed several types of learner behaviors for which he developed models. When learners interacted with educational software, he was able to describe certain learner behavioral patterns (number of clicks, wait time between clicks, order of clicks, etc.) in such a way that if the data log files were analyzed for any learner, those behaviors could be spotted. He could identify when a learner was exhibiting behaviors such as:
Photo by Tim Duckett (tim_d)

• carelessness


• off-task activity


• gaming the system


• avoiding help when they needed it


• not asking for help because they didn't need it


• guessing

I'd be very interested in hearing more about the model development step, and how the researchers constructed meaning from a pattern of clicks.

There are two observations that I took away from the presentation.

1) Model-building is inherently value-laden. Baker alluded to this a bit when he mentioned that learner behaviors that were observed in one of his modelling tests were vastly different in the Phillipines as compared to the United States. Learner behaviors are conditioned by culture and situated in a culture. As educators, we too are the product of our culture and cannot avoid building our assumptions into every tool and system that we develop. Since these data models are used to classify learners, model builders need to approach their task with the utmost humility and care.

2) The educational data mining approach seems to be an example of behaviorism. There was much discussion about the observable behaviors of the learners but not much about the learners' internal thoughts and feelings. Baker did allude a bit to thoughts and feelings of U.S. and Phillipines students when they interacted with his Scooter software. For me, that was the most interesting part of the presentation.

#LAK12 Google vs. your English 101 instructor

LA is fundamentally ethically different from the analysis done by online vendors because of power differentials. The relationship between you and your English 101 instructor, or your institution of higher education as a whole, is much different than your relationship between you and Google or Facebook or Amazon.

When online, we often operate under the assumption that our activities are occurring in our bedrooms, so to speak. We don’t always consciously operate as if our online activities are actually occurring out in the scrutinizable open – although we should be aware of that, if we read the fine print in the terms of agreement. Yet, if they are aware, most people are OK with knowing that Google/Amazon/Facebook is mining their every tweet. Most of us don't know anybody working for those corporations, and even if we did, we know that we are one of multimillions...our online actions are just a drop in the big data bucket.

However, the dynamics between a student and the institution of higher education in which he/she is enrolled is quite different. In this case, the people analysing their data trail may actually KNOW them, and may have the power to award grades, offer or withhold a job reference, or dole out scholarships, work-study jobs, or internship leads. Now, our data is subject to the institutional gaze, and those eyes do have the power to reward and punish. It is a power differential tilted markedly toward the institution. Learning analytics may help a student persist along the path towards college completion, but I believe that the analysands' feelings about being scrutinized by individuals in positions of power (including their instructors) at a university would be much different than their feelings about Google or Facebook mining even that same pot of data.