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This study investigates learner’s reading behaviors in a critical reading task in humanities course using learning analytics techniques. A Critical Analysis of Literature and Cinema course was selected as a context. The course activities evolved over 10 years, and for this instance, some face-to-face classroom critical reading activities were migrated to online mode by using BookRoll, a learning analytics enhanced eBook platform. Students (n=22 out of the 50 registered) accessed Hayavadana, an Indian play uploaded on BookRoll, and attempted to identify performative elements and cultural references in the text and highlight them. In this study, we analyze learner’s reading logs gathered in the learning record store linked to BookRoll during that activity. We extend our previous work where we identify four online reading profiles: effortful, strategic, wanderers, and check-out, based on learner’s clickstream interactions and time spent with the content. We validate the profiles with qualitative interview data collected from the learners and illustrate the quantified learning behaviors of each of those profiles based on an engagement metric. Our work aims to initiate further discussion related to the application of learning analytics in humanities courses both to probe into the learning behaviors of the students and thereby enhance the experiences with the use of interactive learning environments and data-driven services.

Dissertação de mestrado em Estatística, Com o passar do tempo, é cada vez mais importante para as empresas analisarem atentamente o comportamento dos seus clientes, pois dele depende o desempenho de qualquer empresa. Mais do que conhecer, é necessário prever o seu comportamento no futuro para que sejam tomadas decisões de negócio. O Engagement Score (ES) é uma métrica utilizada para medir o envolvimento de um cliente com uma marca/ empresa, que no contexto da área de retalho alimentar desta empresa é obtida a partir de uma probabilidade. Para o cálculo desta métrica, foram inicialmente construídos clusters para se definir quais os clientes mais envolvidos à marca usando variáveis de negócio, que caracterizam o cliente segundo o seu comportamento de compra. Numa segunda fase, e percebendo quais os clientes mais envolvidos, foram aplicados modelos de regressão logística, que estimaram a probabilidade de o cliente estar envolvido com a empresa. Para cada cliente, foi calculada a métrica que mede o seu envolvimento com a empresa. Todos os clientes foram divididos em cinco segmentos e posteriormente caracterizados, conhecendo assim um pouco melhor os clientes presentes em cada um deles. Aquilo que se verifica, é que os clientes mais leais, são clientes que não se resumem apenas às insígnias Continente, procurando comprar nas restantes marcas do ecossistema. São clientes que vivem sobretudo em cidades do litoral do país, em cidades com maior poder de compra e com filhos no agregado familiar. O conhecimento do Engagement Score permite à empresa ter um maior conhecimento sobre quais são os seus clientes “mais valiosos”, tornando-se mais eficaz nas estratégias de marketing aplicadas, de forma a aumentar a fidelidade dos clientes. Os resultados obtidos desta dissertação serão aplicados na empresa de retalho e irão sustentar a tomada de decisão nas estratégias do cliente., Over time, it is increasingly important for companies to carefully analyze the behavior of their customers, as it is central to the performance of any company. More than knowing, it is necessary to predict their behavior in the future so that key decisions can be made in favor of the company. The Engagement Score (ES) is a metric used to measure the involvement of a customer with a brand / company, which in the context of the food retail area of this company is obtained from a probability. For this metric’s calculation, clusters were initially built to define which customers are most involved in the brand using business variables, which characterize the customer according to their buying behavior. In a second phase, and realizing which customers were most involved, logistic regression models were applied, which estimated the probability of the customer being involved with the company. For each customer, the metric that measures their involvement with the company was calculated. All customers were divided into five segments and later characterized, thus knowing a little better the customers present in each one of them. What happens is that the most loyal customers are those who are not limited to the Continente brands, looking to buy in the remaining brands of the ecosystem. They are clients who live mainly in towns on the country’s coast, in cities with greater purchasing power and with children in the household. Knowledge of the Engagement Score enables the company to gain a better understanding of who their ”most valuable” customers are, and in this way becoming more effective in applied marketing strategies in order to increase customer loyalty. The results obtained from this dissertation will be applied in the retail company and will support the decision making in the client’s strategies.