Trust and Distrust in Big Data Recommendation Agents

Big data technology allows for managing data from a variety of sources, in large amounts, and at a higher velocity than before, impacting several traditional systems, including recommendation agents. Along with these improvements, there are concerns about trust and distrust in RA recommendations. Much prior work on trust has been done in IS, but only a few have examined trust and distrust in the context of big data and analytics. In this vein, the purpose of this study is to study the eight antecedents of trust and distrust in recommendation agents’ cues in the context of the Big Data ecosystem using an experiment. Our study contributes to the literature by integrating big data and recommendation agent IT artifacts, expanding trust and distrust theory in the context of a big data ecosystem, and incorporating the constructs of algorithm innovativeness and process transparency

Quality Indicators for Learning Analytics

This article proposes a framework of quality indicators for learning analytics that aims to standardise the evaluation of learning analytics tools and to provide a mean to capture evidence for the impact of learning analytics on educational practices in a standardised manner. The criteria of the framework and its quality indicators are based on the results of a Group Concept Mapping study conducted with experts from the field of learning analytics. The outcomes of this study are further extended with findings from a focused literature review

Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations

Online education platforms play an increasingly important role in mediating the success of individuals’ careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal recommended learning opportunities, according to the platform principles, context, and pedagogy. Though the importance of ensuring equality of learning opportunities has been well investigated in traditional institutions, how this equality can be operationalized in online learning ecosystems through recommender systems is still under-explored. In this paper, we shape a blueprint of the decisions and processes to be considered in the context of equality of recommended learning opportunities, based on principles that need to be empirically-validated (no evaluation with live learners has been performed). To this end, we first provide a formalization of educational principles that model recommendations’ learning properties, and a novel fairness metric that combines them to monitor the equality of recommended learning opportunities among learners. Then, we envision a scenario wherein an educational platform should be arranged in such a way that the generated recommendations meet each principle to a certain degree for all learners, constrained to their individual preferences. Under this view, we explore the learning opportunities provided by recommender systems in a course platform, uncovering systematic inequalities. To reduce this effect, we propose a novel post-processing approach that balances personalization and equality of recommended opportunities. Experiments show that our approach leads to higher equality, with a negligible loss in personalization. This paper provides a theoretical foundation for future studies of learners’ preferences and limits concerning the equality of recommended learning opportunities

The Influence of Online Product Recommendations on Consumer Choice-Making Confidence, Effort, and Satisfaction

The number of products and services available online is growing at a tremendous pace. Consumers increasingly desire the ability to filter through the noise and quickly discover the products that are most relevant to their needs. Many businesses are implementing product recommender systems to provide this ability to consumers, and the result is often increased sales and more satisfied customers. However, recommender systems can also have negative consequences for consumers. For example, a recommender system can bias consumers to purchase more expensive products. Additionally, theories of consumer choice-making suggest that recommender systems can sometimes make purchase choices more difficult, resulting in outcomes that are contrary to the intended purposes of the system, such as customers expending greater shopping effort and feeling less satisfied as a result of receiving too many suggestions. The purpose of this dissertation is to further explore when recommender systems can negatively affect consumers’ online shopping experiences. I investigate three research questions: 1) When do product recommendations increase, rather than decrease, shopping effort? 2) When do product recommendations decrease, rather than increase, shopping satisfaction? And 3) When do recommender systems decrease, rather than increase, consumers’ choice-making confidence? I propose to study these questions by conducting an experiment using a fictitious retail website and online survey

Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach

Se considera que Learning Analytics (LA) es una estrategia prometedora para abordar los desafíos educativos persistentes en América Latina, como las disparidades de calidad y las altas tasas de deserción. Sin embargo, las universidades latinoamericanas se han retrasado en la adopción de LA en comparación con las instituciones de otras regiones. Para comprender las necesidades de los interesados ​​de los servicios de AL, este estudio utilizó métodos mixtos para recopilar datos en cuatro universidades latinoamericanas. Se obtuvieron datos cualitativos de 37 entrevistas con gerentes y 16 grupos focales con 51 docentes y 45 estudiantes, mientras que los datos cuantitativos se obtuvieron de encuestas respondidas por 1884 estudiantes y 368 docentes. De acuerdo con la triangulación de ambos tipos de evidencia, encontramos que (1) los estudiantes necesitan comentarios de calidad y apoyo oportuno, (2) el personal docente necesita alertas oportunas y evaluaciones de desempeño significativas, y (3) los gerentes necesitan información de calidad para implementar intervenciones de apoyo. Por lo tanto, LA ofrece la oportunidad de integrar la toma de decisiones basada en datos en las tareas existentes. © 2020 Elsevier Inc.Learning Analytics (LA) is perceived to be a promising strategy to tackle persisting educational challenges in Latin America, such as quality disparities and high dropout rates. However, Latin American universities have fallen behind in LA adoption compared to institutions in other regions. To understand stakeholders' needs for LA services, this study used mixed methods to collect data in four Latin American Universities. Qualitative data was obtained from 37 interviews with managers and 16 focus groups with 51 teaching staff and 45 students, whereas quantitative data was obtained from surveys answered by 1884 students and 368 teaching staff. According to the triangulation of both types of evidence, we found that (1) students need quality feedback and timely support, (2) teaching staff need timely alerts and meaningful performance evaluations, and (3) managers need quality information to implement support interventions. Thus, LA offers an opportunity to integrate data-driven decision-making in existing tasks. © 2020 Elsevier Inc

The sound of music: from increased personalization to therapeutic values

Music providers like Spotify leverage music recommendation systems to connect users with relevant music. Based on content-based and collaborative-filtering statistical methods, these machine learning algorithms quantify user-song probabilities and present the highest-ranked songs. However, most music providers do not fully address their users’ music seeking and retrieval needs. Likewise, the fields of Recommender Systems (RecSys), Music Recommendation Systems (MRS) and Music Information Retrieval (MIR) remain disconnected from real-world use cases of music seeking. In this conceptual paper, we review the literature of the RecSys, MRS, MIR and Music Therapy (MT) academic fields. We discuss trends towards greater user control and personalization in the MRS and MIR fields and the connections between MT and positive health outcomes such as reductions in stress, anxiety and heart rate.Analysis. We argue that greater control and visibility into the characteristics of songs and recommended items can generate positive downstream benefits. We recommend features that empower users to better seek, find, store, retrieve and learn from their musical catalogs. We suggest design enhancements that recognize music’s wider psychological and physiological benefits and create opportunities to build domain knowledge. Unlocking music’s myriad benefits through the enhancements proposed would catalyze positive outcomes for business stakeholders, users and society.Peer Reviewe