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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agosto 17, 2016 § Deixe um comentário
A empresa em que você trabalha te deu um smartphone. Você está dando uma checada nele quando percebe um daqueles e-mails do LinkedIn: “estas empresas estão procurando candidatos como você”. Apesar de não estar particularmente interessado(a) em algo, mas sempre aberto(a) a oportunidades – e um tanto quanto curioso(a) – você clica no link. Alguns minutos depois, seu chefe aparece na sua mesa e diz: “notamos que você tem passado mais tempo no LinkedIn ultimamente, vamos conversar a respeito da sua carreira e se está feliz conosco”?
É um cenário digno de big brother, mas não tão improvável. É sabido que o custo de se trocar um funcionário nunca é barato (nem o de mantê-lo), mas em muitos setores, o custo de se perder bons funcionários está incrivelmente maior por conta da natureza cada vez mais colaborativa dos postos de trabalho. Este é inclusive um dos efeitos colaterais do trabalho em equipe, quando se forma uma “bem azeitada”, não é nada trivial trocar um “jogador”. Desta forma, é até natural que empresas intensifiquem seus esforços em prever os riscos de algum funcionário “abandonar o barco”. As táticas usadas variam da pura e simples “espionagem” a análises de padrões de atividade em rede sociais.
Não quero entrar em discussões a respeito da moralidade da prática ou mesmo da sua legalidade. Em muitos sentidos, os dados gerados na internet ainda são (e talvez o sejam por muito tempo) como “águas internacionais” – pode-se até envolver algum tipo legislação para tentar regular sua utilização – mas é incrivelmente difícil garantir a sua efetiva aplicação. Sem contar ainda com os dados produzidos dentro de uma organização – que indiscutivelmente são dela. O ponto que gostaria de abordar gira em torno dos métodos e medidas que um número cada vez maior de empresas tem tomado para identificar os riscos de se perder um “colaborador”.
As principais razões têm-se mantido estáveis por anos: problemas com os chefes; falta de oportunidade de crescimento; um emprego mais desafiador ou melhor salário. Uma nova pesquisa, conduzida pela CEB – uma empresa de pesquisa tecnológica – com sede em Washington, decidiu focar sua análise não apenas no “por que”, mas também no “quando”. Segundo o diretor da empresa, Brian Kropp, o que estimula alguém a querer mudar de emprego é a comparação que a pessoa faz de como está em relação aos seus conhecidos ou então como deseja estar em determinado momento da vida. O interessa da CEB era saber quais momentos estimulavam a comparação.
Algumas descobertas não trazem muitas surpresas, “aniversários de empresa” (antigamente conhecido como “tempo de casa”) são momentos naturais para reflexões e o aumento de 6% a 9% na procura de novos empregos nesta época confirmam a crença. Momentos sem ligação direta com o trabalho também são incentivos para autoavaliações, como aniversários – principalmente de números redondos, como 40 ou 50 anos – aumento de 12% na procura. Encontros de turma (colégio, faculdade, etc.) também incentivam a busca por “novas oportunidades” (aumento de 16%).
Voltando ao monitoramento, a maior possibilidade de acesso à gigantesca quantidade de dados que produzimos diariamente (o cada vez mais famoso big data) e em especial ao que é conhecido como dark data – que de maneira similar à “matéria escura” da física, constitui a maior parte dos dados de qualquer organização e que quase nenhuma delas se interessava em conhecer – tem permitido identificar possíveis padrões de comportamento de funcionários que pensam em sair. O já citado e-mail LinkedIn é um exemplo. Outro comum é o monitoramento do crachá (conhecido como badge swipe), que verifica o uso do crachá para entrada e saída do prédio (ou da garagem) e identifica padrões que possam sugerir uma “escapada” para entrevista. Se parece exagero a princípio, saiba que algumas empresas, como a Jobrate, têm se especializado neste tipo de análise e prestam consultoria para inúmeras multinacionais. Grandes investidores também têm baseado suas estratégias de investimento levando em conta informações que sugerem mudanças em posições chave nas empresas as quais estão interessados.
Uma perspectiva bem tensa, não? Mas é preciso se lembrar que nem tudo deve ser encarado como “teoria da conspiração”. É claro que as informações podem ser usadas em relações de “mais valia” (esta tirei do fundo da cartola), afinal estamos lidando com seres-humanos, mas não é este o enfoque. Empresas como a Credit Suisse, usa suas informações para melhorar seu relacionamento com funcionários “insatisfeitos”. Como base nelas, por exemplo, avisa funcionários sobre vagas disponíveis em outros setores ou a respeito de oportunidades internas. Com isto, a empresa estima ter economizado de US$ 75 milhões a US$ 100 milhões em custos de recrutamento, seleção e treinamento, somente em 2014.
Ações preventivas para se manter um funcionário parecem ser um “melhor negócio” do que, por exemplo, esperar “a coisa acontecer” e fazer uma contraoferta. Os dados da CEB mostram que cerca de 50% dos funcionários que decidem ficar por conta de uma contraoferta, acabam saindo nos 12 meses seguintes. A maneira como as informações geradas por estas análises de dados está sendo utilizada atualmente, sugere que o big brother é invertido. Manter na casa ao invés de eliminar.
Fonte: pesquisa CEB, “The New Path Forward: Creating Compelling Careers for Employees and Organizations,”