novembro 21, 2016 § Deixe um comentário
There’s a song by Leonard Cohen that states “everybody knows” and “that’s how it goes”. The same goes for the fact that the amount of data online activities generate is skyrocketing. This is true because more and more of our commerce, entertainment, and communication are occurring over the Internet and despite concerns about globalization and information accuracy, it’s a trend that is impossible to curb. Like a steamrolling, this data tsunami touches us all, so it’s more than natural that it also catches education. With analytics and data mining experiments in education starting to proliferating, sorting out fact from fiction and identifying research possibilities and practical applications becomes a necessity.
Educational data mining and learning analytics work based on assumption of patterns and prediction. Both disciplines are used to research and build models in several areas that influence online learning systems. The bottom-line here is if we can discern the pattern in the data and make sense of what is going on, we can predict what should come next and take the appropriate action. The business world name it insight and it’s the difference of make “big bucks” or be caught unprepared. So believe me, it’s valuable.
Data mining with educational purposes can be used basically in two big areas. One is user modelling, which encompasses what a learner knows, what a learner’s behavior and motivation are, what the user experience is like, and how satisfied users are with online learning. Well, the same kind of data used to model can be used to profile users. Profiling means grouping similar users into categories using salient characteristics. These categories then can be used to offer experiences to groups of users or to make recommendations individually and proceed adaptations to how an online learning system performs.
A little explanation it’s needed at this point: online learning systems refer to online courses or to learning software or interactive learning environments that use intelligent tutoring systems, virtual labs, or simulations. They may be offered through a learning or course management system and through a learning platform. When online learning systems use data to change in response to student performance, they become adaptive learning environments.
Increasing use of online learning offers some opportunities, such as to integrate assessment and learning and gather information in nearly real time, to improve future instruction. This process goes like this: as students work, the system captures their inputs, collecting evidence of activities, knowledge, and strategy used. Everything counts here, the information each student selects or inputs, the number of attempts the student makes, the allocation of time across parts of the process, and the number of hints and feedback given.
As students can benefit from detailed learning data, so the broader education community can thrive from an interconnected feedback system – such as what works better for a particular content and how to stimulate necessary skills like metacognition. As put by the U.S. Department of Education in a 2010 report (National Education Technology Plan – NETP, 2010a, p. 35): “The goal of creating an interconnected feedback system would be to ensure that key decisions about learning are informed by data and that data are aggregated and made accessible at all levels of the education system for continuous improvement”.
As it’s expected that these learning systems be able to exploit in detail activity data from learners to recommend what the next activity should be, and also to predict how a particular student will perform in future learning activities, being able to connect the dots and produce insights presents itself as a necessity. It’s precisely here that enters data mining and learning analytics.
Understanding big data
Although using data to enhance decision processes is not new – they are used in what is known as business intelligence or analytics – it’s a relatively new approach concerning education. As their business counterparts, learning analyses can discern historical patterns and trends from data and create models that predict future trends and patterns and comprise applied techniques from computer science, mathematics, and statistics in order to extract usable information from very large datasets.
Usually, data are stored into a structured format, which are easy for computers to manipulate. However, the data gathered from learning platforms have a semantic structure that is difficult to discern computationally without human aid, hence is called unstructured data (e.g. texts or images). To analyze these events is required techniques that work with unstructured text and image data and data from multiple sources. When these data comprise a vast amount, we have the famous big data. It’s important to understand that big data does not have a fixed size, it’s a concept. As any given number assigned to define it would change as computing technology advances to handle more data, big data is defined relative to current capabilities.
Big data, educational data mining and learning analytics
The big amount of data snared from online behavior feeds algorithms and enables them to infer the users’ knowledge, intentions, and interests and to build models that can predict future behavior and interest. In order to achieve this goal data mining and analytics are applied as the fields of educational data mining and learning analytics. Although there is no hard distinction between these two, they have had different research histories and distinct research areas.
In general, educational data mining (also known as EDM) looks for new patterns in data and develops new algorithms and models, using statistics, artificial intelligence, and (of course) data mining to analyze the data collected during teaching and learning. Learning analytics, for instance, applies known predictive models in instructional systems, using different knowledge, such as information science, sociology and psychology, as well as statistics, AI, and data mining in order to influence educational practice.
Educational data mining
Diving a little bit into the subject, the need for understanding how students learn is the major force behind educational data mining. The suite of computational and psychological methods and research approaches supported by interactive learning methods and tools, such as intelligent tutoring systems, simulations, games, have opened up opportunities to collect and analyze student data and to discover patterns and trends in those data. Data mining algorithms help find variables that can be explored for modelling and by applying data mining methods that classify data and find relationships, these models can be used to change what students experience next or even to recommend outside academic assignments to support their learning.
An important feature of educational data is that they are hierarchical. All the data (from the answers, the sessions, the teachers, the classrooms, etc.) are nested inside one another. Grouping it by time, sequence, and context provide levels of information that can show the impact of the practice sessions length or the time spent to learning – as well as how concepts build on one another and how practice and tutoring should be ordered. Providing the right context to these information help to explain results and to know where the proposed instructional strategy works or not. The methods that have been important to stimulate developments in mining educational data are those related:
1) To prediction, for understanding what behaviors in an online learning environment, such as participation in discussion forums and taking practice tests, can be used to predict outcome such as which students might fail a class. It helps to develop models that provide insights that might help to better connect procedures or facts with the specific sequence and amount of practice items that best stimulate the learning. It also helps to forecast or understand student educational outcomes, such as success on posttests after tutoring.
2) To clustering, meaning to find data points that naturally group together and that can be used to split a full dataset into categories. Examples of clustering are grouping students based on their learning difficulties and interaction patterns, or grouping by similarity of recommending actions and resources.
3) To relationship, meaning discover relationships between variables in a dataset and encoding them as rules for later use. These techniques can be used to associate student activity (in a learning management system or discussion forums) with student grades, to associate content with user types to build recommendations for content that is likely to be interesting or even to make changes to teaching approaches. This latter area, called teaching analytics, is of growing importance and key to discover which pedagogical strategies lead to more effective or robust learning.
4) To distillation, which is a technique that involves depicting data in a way that enables humans to quickly identify or classify features of the data. This area of educational data mining improves machine learning models by allowing humans to identify patterns or features easier, such as student learning actions, student behaviors or collaboration among students.
5) To model discovery, which is a technique that involves using a validated model (developed through such methods as prediction or clustering) as a component in further analysis. Discovery with models supports discovery of relationships between student behaviors and student characteristics or contextual variables, analysis of research questions across a wide variety of contexts, and integration of psychometric modeling into machine learned models.
Learning analytics emphasizes measurement and data collection as activities necessary to undertake, understand, analyze and report data with educational purposes. Unlike educational data mining, learning analytics generally does not emphasize reducing learning into components but instead seeks to understand entire systems and to support human decision making. Draws on a broad array of academic disciplines, incorporating concepts from information science, computer science, sociology, statistics, psychology, and learning sciences.
The goal is to answer important questions that affect the way students learn and help us to understand the best way to improve organizational learning systems. Therefore, it emphasizes models that could answer questions such as:
- When are students ready to move on to the next topic?
- When is a student at risk for not completing a course?
- What is the best next course for a given student?
- What kind of help should be provide?
As a visual representation of analytics is critical to generate actionable analyses, the information is often represented as “dashboards” that show data in an easily digestible form. Although the methods used in learning analytics are draw from those used in educational data mining, it may employ additionally social network analysis (to determined student-to-student and student-to-teacher relationships and interactions that help to identify disconnected students, influencers, etc.) and social metadata to determine what a user is engaged with.
As content moves online and mobile devices for interacting with content enable a 24/7 access, understand what data reveal can lead to fundamental shifts in teaching and learning systems as a whole. Learners and educators at all levels can draw benefits from understanding the possibilities of the use of big data in education. Data mining and learning analytics are two powerful tools that can help shape the future of human learning.
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novembro 17, 2016 § Deixe um comentário
If you are into statistics probably already know the importance of regression analysis to statistical modelling. If you are not, it is necessary to say that it is important stuff and is use for estimating the relationships among variables. There are many techniques and extensions for carrying out regression analysis such as linear regression, multivariate linear regression (also known as general linear model), some variances as Bayesian multivariate linear regression, least-squares and so on.
What these approaches have in common is an equation of the form y = a + bx, where x is the explanatory variable and y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).
Harold V. Henderson and Paul F. Velleman provided a famous example of the use of a regression model in their paper “Building Multiple Regression Models Interactively”, published in 1981 by Biometrics magazine (to those whom are interested in read the original one, check http://www.mortality.org/INdb/2008/02/12/8/document.pdf).
There they used what is known as the “Gasoline Mileage Data”, which became a dataset used around the world for educational purposes. The data were extracted from 1974 Motor Trend magazine and comprise gasoline mileage in miles per gallon (MPG), and ten aspects of automobile design and performance for 32 automobiles (1973-74 models). I explored this data using the dataset created for R programming called “mtcars”. As I believe that any analysis has to have a purpose, mine attempted to determine whether an automatic or manual transmission is better for MPG and to quantify the MPG difference.
In doing so, I composed the following paper with linear and multiple regression models and the codes to perform the modelling in R, as well as my personal analysis. The paper can be accessed at http://rpubs.com/marcelo_tibau/228029
novembro 11, 2016 § Deixe um comentário
To those whom are eager to know more about Machine Learning and how it goes in a real life work, I share a paper I wrote with analysis, codes and algorithms of a Machine Learning Prediction Assignment. I wrote the codes in R, which is a statistical programming language. I also would like to thank PUC-Rio for providing the dataset that I worked.
Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. In this project, your goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways.
The data for this project came from the Human Activity Recognition study, conducted by Pontifícia Universidade Católica – Rio de Janeiro.
Ugulino, W.; Cardador, D.; Vega, K.; Velloso, E.; Milidiu, R.; Fuks, H. Wearable Computing: Accelerometers’ Data Classification of Body Postures and Movements. Proceedings of 21st Brazilian Symposium on Artificial Intelligence. Advances in Artificial Intelligence – SBIA 2012. In: Lecture Notes in Computer Science. , pp. 52-61. Curitiba, PR: Springer Berlin / Heidelberg, 2012. ISBN 978-3-642-34458-9. DOI: 10.1007/978-3-642-34459-6_6.
It can be accessed at:
novembro 10, 2016 § Deixe um comentário
A indústria da tecnologia defendeu por anos o argumento de que para se ter uma economia baseada na inovação, era preciso que se estimulasse a educação, o conhecimento aplicado à propriedade intelectual e o multiculturalismo. Isto impactou diretamente em políticas públicas e legislação de diversos países em relação à organização e às metodologias de seu sistema educacional, proteção à propriedade intelectual e estímulo ao desenvolvimento de pesquisas científicas (que geram propriedade intelectual) e em políticas de imigração (em especial as ligadas à concessão de visto de trabalho para os chamados skilled workers).
Os EUA, como uma das grandes forças impulsionadoras da indústria tech, sempre foram vistos como determinante para a definição das posturas deste mercado no mundo todo. É natural então, que uma presidência Trump – potencializada pelo Brexit – não podemos esquecer que o Reino Unido é o segundo produtor mundial de propriedade intelectual, atrás apenas dos EUA, leve a uma reavaliação estratégica da área em relação as suas políticas. Já começaram a circular e-mails pelo Vale do Silício, propondo o reposicionamento para a defesa do corte de impostos para a área e o comprometimento em relação à repatriação de divisas.
Entendo a postura e reconheço a necessidade de reposicionamento – em especial se levarmos em consideração que Trump declarou em campanha que iria iniciar uma ação antitruste contra a Amazon e prometeu forçar a Apple a fabricar seus produtos nos EUA. Mas um dos argumentos mais poderosos das empresas de tecnologia em relação à sua própria importância, sempre foi o fato de que suas metas não eram apenas financeiras, mas abarcavam a construção de um futuro progressista. Sim, queriam dinheiro, mas também queriam construir um mundo melhor em termos filosóficos e democráticos – protegiam a educação e o conhecimento como modo de empoderar as pessoas e estimulá-las a quererem se tornar mais inteligentes e cultas. A lógica era que pessoas mais inteligentes tinham mais possibilidades de inovar.
Thomas Friedman – o autor do livro “O Mundo é Plano”, que propiciou muita da base conceitual para os argumentos defendidos pela indústria tech – escreveu sobre o resultado das eleições americanas, no texto intitulado “Homeless in America”, que o chamado “aprendizado para a vida toda” (no original lifelong learning) poderia ser uma fonte inesgotável de stress para algumas pessoas.
O risco que esta visão de mundo coloca é: se o aprendizado pode fazer mais mal do que bem e se algumas pessoas, não apenas o rejeitam, mas agem conscientemente para impedir a formação de um ambiente que estimule o desenvolvimento da sua fonte (o conhecimento), por que priorizar a educação?
40% das pesquisas científicas realizadas pelo Reino Unido eram financiadas pela União Europeia (rejeitada pela maioria dos britânicos). Facebook e Twitter têm sido apontados como causadores do declínio do jornalismo e da irrelevância dos fatos (e de quebra contribuído para a expansão do trolling, racismo e misoginia que caracterizaram a campanha do agora presidente Trump). O crescimento de um sentimento anti-tech pode, de verdade, mudar a direção que as políticas educacionais vinham tomando nos países desenvolvidos (que queira ou não, dão o tom para o restante do mundo).
O efeito colateral pode ser a criação de uma elite intelectual tecnológica – porque a indústria continuará e precisará de pessoas que tenham a habilidade de criar propriedade intelectual. Mas, talvez o sonho de democratizar esta habilidade tenha acabado.