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As educators seek ways to enhance student motivation and improve achievement, promising advances are being made in adaptive approaches to instruction. Learning technologies are emerging that promote a high level of personalization of the learning experience. One type of personalization is context personalization, in which instruction is presented in the context of learners’ individual interests in areas like sports, music, and video games. Personalized contexts may elicit situational interest, which can in turn spur motivational and metacognitive states like positive affect and focused attention. Personalized contexts may also allow for concepts to become grounded in prior knowledge by fostering connections to everyday activity. In this Chapter, we discuss the theoretical, design, and implementation issues to consider when creating interventions that utilize context personalization to enhance motivation.

First, we provide an overview of context personalization as an instructional principle and outline the emerging evidence that personalization can enhance motivation and improve achievement. We then discuss the theory hypothesized to account for the effectiveness of context personalization and discuss the approaches to personalization interventions. We close by discussing some of the practical issues to consider when bridging the design and implementation of personalization interventions. Throughout the paper, we anchor our discussion to our own research which focuses on the use of context personalization in middle and high school mathematics.

The theoretical mechanisms through which context personalization enhances learning may include (1) eliciting positive affective reactions to the instruction, (2) fostering feelings of value for the instructional content through connections to valued personal interests, or (3) drawing upon prior funds of knowledge of the topic. We provide hypotheses for the relatedness of context personalization to triggering and maintaining situational interest, and explore potential drawbacks of personalization, considering research on seductive details, desirable difficulties, and authenticity of connections to prior knowledge. We further examine four approaches to personalized learning – “fill-in-the-blank” personalization, matching instruction to individual topic interests, group-level personalization, and utility-value interventions. These approaches vary in terms of the depth of the personalization – whether simple, shallow connections are made to interest topics, or deep, meaningful connections are made to learners’ actual experiences. The consideration of depth also interacts with grain size – whether content is personalized based on the broader interests of a group, or the individual experiences of a particular learner. And finally, personalization interventions can have different levels of ownership – an instructor can generate the personalized connections, the connections can be made by the curriculum designers, or learners can take an active role in personalizing their own learning. Finally, we discuss the practical implementation issues when bringing context personalization interventions into K-12 classrooms. Personalization can be logistically difficult to implement, given that learners hold a diverse array of interests, and may experience each of those interests differently. In addition, particular types of instructional content may show greater sensitivity when personalization is implemented, and personalization may be most helpful for learners with certain background characteristics.

Realizing the promise of personalized learning is an unsolved problem in education whose solution becomes ever more critical as we confront a new digital age. Context personalization has the potential to bring together several well-established strands of research on improving student learning – research on the development of interest, funds of knowledge, and utility value – into one powerful intervention.

This chapter reviews the recent developments at the intersection of personalization and AI in marketing and related fields. We provide a formal definition of personalized policy and review the methodological approaches available for personalization. We discuss scalability, generalizability, and counterfactual validity issues and briefly touch upon advanced methods for online/interactive/dynamic settings. We then summarize the three evaluation approaches for static policies – the Direct method, the Inverse Propensity Score (IPS) estimator, and the Doubly Robust (DR) method. Next, we present a summary of the evaluation approaches for special cases such as continuous actions and dynamic settings. We then summarize the findings on the returns to personalization across various domains, including content recommendation, advertising, and promotions. Next, we discuss the work on the intersection between personalization and welfare. We focus on four of these welfare notions that have been studied in the literature: (1) search costs, (2) privacy, (3) fairness, and (4) polarization. We conclude with a discussion of the remaining challenges and some directions for future research.

The aim of this chapter is to investigate the transformations undergone by dual marketers in their marketing strategies, with the advent of new digital tools. In particular, the chapter wants to emphasise the shift from a dual marketing perspective to the omnichannel approach, based on the integration of communication strategies between business-to-business (B2B) and business-to-consumer (B2C). To this end, the chapter first analyses the theoretical contribution of the integrated marketing communication paradigm in omnichannel marketing. Then, identifying the characteristics common to the B2B and B2C markets, it maps the use of touchpoints and digital content in the dual marketers' omnichannel strategies. Moreover, the role of online personalised marketing and big data analytics tools is illustrated in order to investigate the relationship between dual marketing and big data analytics towards Industry 4.0. Lastly, the dynamics and the perspectives emerged from the literature are traced within the case study of Unicredit Italia to try to bring out the best practices for an omnichannel strategy of dual marketers.