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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 will explore how the places of learning might look in next generation learning spaces where learners traverse physical and virtual spaces using personalised learning strategies. It will examine how learning spaces may represent ubiquitous spaces in which the learner undertakes some form of study or learning. Although there has been extensive examination of the design of spaces for knowledge generation (Keppell & Riddle, 2012, 2013; Souter, Riddle, Sellers, & Keppell, 2011) there has been little attention given to how learners customise and personalise their own physical and virtual learning spaces as they traverse their learning journey. Seven principles of learning space design will be adapted for use by the personalised learner. Personalised learning strategies encompass a range of knowledge, skills and attitudes that empower the learner to take charge of their learning within next generation learning spaces. Personalised learning consists of six broad concepts: digital citizenship, seamless learning, learner engagement, learning-oriented assessment, lifelong and life-wide learning and desire paths. Teachers will need to assist learners to design their own personalised learning spaces throughout formal education to encourage learners to be autonomous learners throughout their lifetime. In order to assist learners in developing personalised learning strategies we need to teach them about learning space literacies. We can’t assume learners have the knowledge, skills and attitudes to be able to identify and effectively utilise appropriate learning spaces that optimises engagement.

This chapter explores children’s agency in using mobile technologies at home and in school. Supporting children’s agency has been offered as a rationale for adopting personalised education worldwide. Children’s agency is also drawn upon as a justification for children’s use of personal mobile devices. This chapter considers children’s agency in light of the personalised education in one UK primary school and the children’s use of mobile technologies at school and at home. The findings are based on eight days of observations of classroom practice and interviews with six case study children in the Year 6 classroom. In sessions that were supported with mobile technologies, children’s learning was personalised to each child, but constrained by the amount of time that the activity lasted and that the technology was available for. Based on children’s accounts, their use of mobile technologies at home was constrained by their parents’ restrictions and monitoring practices. The chapter discusses the reality of children’s agency in light of adults’ mediation and children’s actual experiences of personalised learning.

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.

During the past decade, fast-paced changes created a new environment organisations need to adapt to in an agile way. To support their transformation, organisations are rethinking their approach to learning. They are moving away from traditional instructor-centred, standardised classroom-based learning settings. Instead, learning needs to be tailored to the individuals’ needs, available anywhere at any time and needs to enable learners to build their network. The development of digital tools, specifically network technology and social collaboration platforms, has enabled these new learning concepts.

The use of these new learning concepts in organisations also has implications for higher education. The present case study, therefore, investigates how universities can best prepare future employees and leaders for these new working environments, both on a content level and a methodological level. It also investigates if these new learning concepts can support universities in dealing with a changing environment.

The investigated case is a traditional face-to-face leadership lecture for a heterogeneous group of students. It was reconceptualised as a personalised and social collaborative learning setting, delivered through a social collaboration platform as the primary learning environment. Initial evaluation results indicate positive motivational effects, experience sharing and changes in perception of the student − lecturer relationship. The findings also supported previous challenges of computer-supported collaborative learning settings, such as the perception of a higher cognitive load. The implications of these results for the future teaching and business models of higher education are discussed. In addition, the potential of these computer-supported social collaborative learning settings is outlined.