Personal Analytics Explorations to Support Youth Learning

While personalized learning environments often include systems that automatically adapt to inferred learner needs, other forms of personalized learning exist. One form involves the use of personal analytics in which the learner obtains and analyzes data about himself/herself. More known in informatics communities, there is potential for use of personal analytics for design of instruction. This chapter provides two cases of personal analytics learning explorations to demonstrate their range and potential. One case is of a high school student examining how sleep influences her mood. The other case is of a sixth-grade class of students examining how deviations from typical walking behavior change distributional shape in plotted step data. Both cases show how social support and direct experience with data correction are intimately involved in how youth can learn through personal analytics activities.

Developing a Personalized, Educational Gaming Experience for Young Chinese DLI Learners

In this chapter, a design-based approach was used to investigate the effectiveness of a story-driven game designed to improve elementary Chinese dual language immersion (DLI) learners reading strategies by fostering self-regulated learning. To facilitate reading comprehension and increase vocabulary development, learners are empowered with an in-game notebook which allows them look up and review new vocabulary words. The authors argue that game design features such as the in-game notebook and meaningful in-game choices have the potential to not only motivate learners to persist in reading a second language, but also provide them with the tools needed to regulate and promote their own vocabulary learning. Data were collected from Chinese DLI elementary students who played this game individually with a researcher. These data include log data, screen capture video of gameplay, and post-gameplay interviews. In the findings, successes and failures of the current game design are presented and suggestions for future designs aimed at supporting young Chinese DLI learners are provided.

Computational Thinking and Participatory Teaching as Pathways to Personalized Learning

This chapter discusses personalized learning by briefly outlining historical trends and deficiencies associated with what can be referred to as production style or assembly line approaches to education before contrasting personalized learning definitions. The chapter extends those definitions. It discusses participatory teaching as a personalized learning strategy by which students take on roles of co-teaching, co-designing lessons, or co-designing curriculum with adult teachers. One participatory teaching example involves an international group of students who help one another learn science and mathematics through shared video production. This example involves a US school involved in a larger districtwide effort comprehensively designed to involve each student. Organized around computational thinking, multidisciplinary innovation, arts integration, and collaborative problem-solving, the district may be viewed as a case study in implementing personalized learning. The chapter furnishes several examples that blend participatory teaching and computational thinking.

Intercultural Computer-Supported Collaborative Learning

In the absence of an equitably distributed method for providing immersive intercultural learning experiences, teachers have used digital technologies to personalize domestic learning experiences that cultivate intercultural competence and collaborative skills. This chapter provides a review of intercultural computer-supported collaborative learning, discusses the main issues that students and teachers encounter, and provides a summary of research supporting teacher integration of this instructional approach.

Learner Performance and Satisfaction Level in Personalized Learning Environments

The overall aim of this chapter is to determine the personalizable online learning environments and learner participation in these environments, learner satisfaction in using them, and the effect of the environment on learners' course performance in terms of learning. With this purpose in mind, the researchers have tried to determine the learners' personalization preferences of content order and appearance in online learning environments offered and personalization-related satisfaction and performances. In data collection and analysis processes, both quantitative and qualitative methods were used and the study is designed as mixed model. In conclusion, more than half of the students carried out the personalization procedure and females customized the appearance of their environment in a high frequency rate compared to males. The study indicated that, in general, learners are satisfied with the personalization procedure and there is a significant difference in performances of students who fulfilled the procedure compared to those who did not.

Personalization With Digital Technology

How to personalize learners' learning with digital technology so that learners derive optimal experiences in learning is a key question facing learning scientists, cognitive psychologists, teachers, and professional instructional designers. One of the challenges surrounding personalization and digital technology is how to promote learners' cognitive processes at a deeper level so that they become optimally engaged in critical and creative thinking, making inferences in learning, transferring knowledge to new learning situations, and constructing new knowledge during innovative learning process. This chapter examines the literature relating to deep cognitive processes and the idiosyncratic features of digital technology that support learners' deep cognitive processes in learning. Guidelines pertaining to personalization with digital technology in regard to deep cognitive processing are proposed, followed by the discussions on future research with a focus on verifying the theoretical constructs proposed in the guidelines.

Enabling and Integrating Technology With Personalized Learning

Personalized learning refers to a variety of instructional methods, academic support strategies, and educational programs that are proposed to address specific interests and learning needs of the students. The key goal of integrating technology with personalized learning is to have students progress from high school to college or a career. But currently, most of the students leave high school without the knowledge and technical skills that they need for success in further education and workplace. In order to improve K-12 education (i.e., kindergarten [K] and the 1st through the 12th grade) fundamental and systemic changes in middle and high school education are required. Integration of technology can help the students to improve knowledge, advance skills, and to gain the competencies to work well in the society and workforce. This chapter focuses on applicability of technology, implementation, and feasibility issues that play a key role in personalized learning.

College Students' Perceptions on the Role of Digital Technology and Personalized Learning

This chapter examines college students' perceptions on personalized learning with digital technology. Five hundred and five college students were recruited from American and Chinese institutions of higher education. Participants were given a survey questionnaire on digital technology and personalized digital learning. Using factor analysis, three conceptual constructs were perceived by college students as critical to personalized digital learning. In addition to the critical factors identified, the results also revealed significant relationships between the variables of demographic and computer experience and the constructs perceived by college students. These findings can inform the design and implementation of personalized digital learning in higher education. Recommendations for future research include focusing on the verification of the constructs in both theoretical and practical fields.

Personalized Learning and Online Instruction

This development of digital inclusion with personalized learning has had an impact on how courses are designed and delivered. To that end, a behavioral approach that combines digital with personalized learning is CAPSI (computer-aided personalized system of instruction). In CAPSI, students decide when and where to study course material and where and when to take a test on their learning. The changes occurring in higher education also need to incorporate the development of critical thinking skills. CAPSI is highly adaptable to developing critical or higher-level thinking based on Bloom's taxonomy; CAPSI's emphasis on written answers, providing feedback, and writing appeals leads to higher order thinking. To assess student satisfaction, questionnaires given at the end of a course show that many students find CAPSI to be beneficial to their learning. Also, due to its flexible design, CAPSI is highly modifiable and can be used in all courses in a variety of locations and with students at different educational levels.

A Hybrid Architecture for Adaptive, Intelligent, and Ubiquitous Educational Systems

Ubiquitous learning environments (ULEs) allow real and virtual study materials to be combined to enrich learning experiences. Classrooms equipped with electronic devices produce artifacts that can reconstruct the captured experiences for later use and review. Those environments have the potential to turn themselves into a factory of learning objects (LOs), which may become useless if appropriate means for reaching them are not provided to students. On the other hand, adaptive educational hypermedia (AEH) have appeared as a way of personalizing educational content in web environments and modern intelligent tutoring systems (ITS) provide personalized resources for automating pedagogical tasks. In this way, this chapter explores the concept of ULEs together with AEH and ITS for generating and providing personalized LOs to students. The proposed approach is grounded in artificial intelligence, pedagogical concepts, and computational systems technologies such as ontologies, Bayesian networks, learning styles, and ULEs for creating better individual learning experiences.