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1. Intelligent Technologies for Personalized Practice Systems

   Brusilovsky, Peter (June 2024, Information and technology in education and learning)

   Personalized practice systems focus on supporting self-organized learning in a free practice mode. Adapting to the learners’ knowledge and goals, these systems help them navigate the increasing volumes of smart learning content, guide them to practice opportunities that are most appropriate to their level of knowledge and increase their motivation to practice. In this paper, we distill the experience generated by 20 years of research on personalized practice systems into a set of AI-based technologies that make these systems efficient, engaging, and user-friendly. 

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2. A Systematic Review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)?

   https://doi.org/10.1007/s10648-021-09615-8  

   Bernacki, Matthew L.; Greene, Meghan J.; Lobczowski, Nikki G. (April 2021, Educational Psychology Review)

   null (Ed.)

   Teachers, schools, districts, states, and technology developers endeavor to personalize learning experiences for students, but definitions of personalized learning (PL) vary and designs often span multiple components. Variability in definition and implementation complicate the study of PL and the ways that designs can leverage student characteristics to reliably achieve targeted learning outcomes. We document the diversity of definitions of PL that guide implementation in educational settings and review relevant educational theories that could inform design and implementation. We then report on a systematic review of empirical studies of personalized learning using PRISMA guidelines. We identified 376 unique studies that investigated one or more PL design features and appraised this corpus to determine (1) who studies personalized learning; (2) with whom, and in what contexts; and (3) with focus on what learner characteristics, instructional design approaches, and learning outcomes. Results suggest that PL research is led by researchers in education, computer science, engineering, and other disciplines, and that the focus of their PL designs differs by the learner characteristics and targeted outcomes they prioritize. We further observed that research tends to proceed without a priori theoretical conceptualization, but also that designs often implicitly align to assumptions posed by extant theories of learning. We propose that a theoretically guided approach to the design and study of PL can organize efforts to evaluate the practice, and forming an explicit theory of change can improve the likelihood that efforts to personalize learning achieve their aims. We propose a theory-guided method for the design of PL and recommend research methods that can parse the effects obtained by individual design features within the “many-to-many-to-many” designs that characterize PL in practice. 

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3. BachDuet: A Deep Learning System for Human-Machine Counterpoint Improvisation

   Benetatos, Christodoulos; VanderStel, Joseph; Duan, Zhiyao (July 2020, Proceedings of the International Conference on New Interfaces for Musical Expression)

   During theBaroque period, improvisation was a key element of music performance and education. Great musicians, such as J.S. Bach, were better known as improvisers than composers. Today, however, there is a lack of improvisation culture in classical music performance and education; classical musicians either are not trained to improvise, or cannot find other people to improvise with. Motivated by this observation, we develop BachDuet, a system that enables real-time counterpoint improvisation between a human anda machine. This system uses a recurrent neural network toprocess the human musician’s monophonic performance ona MIDI keyboard and generates the machine’s monophonic performance in real time. We develop a GUI to visualize the generated music content and to facilitate this interaction. We conduct user studies with 13 musically trained users and show the feasibility of two-party duet counterpoint improvisation and the effectiveness of BachDuet for this purpose. We also conduct listening tests with 48 participants and show that they cannot tell the difference between duets generated by human-machine improvisation using BachDuet and those generated by human-human improvisation. Objective evaluation is also conducted to assess the degree to which these improvisations adhere to common rules of counterpoint, showing promising results. 

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4. Sounds of Health: Using Personalized Sonification Models to Communicate Health Information

   https://doi.org/10.1145/3570346  

   Clark, Matthew; Doryab, Afsaneh (December 2022, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   This paper explores the feasibility of using sonification in delivering and communicating health and wellness status on personal devices. Ambient displays have proven to inform users of their health and wellness and help them to make healthier decisions, yet, little technology provides health assessments through sounds, which can be even more pervasive than visual displays. We developed a method to generate music from user preferences and evaluated it in a two-step user study. In the first step, we acquired general healthiness impressions from each user. In the second step, we generated customized melodies from music preferences in the first step to capture participants' perceived healthiness of those melodies. We deployed our surveys for 55 participants to complete on their own over 31 days. We analyzed the data to understand commonalities and differences in users' perceptions of music as an expression of health. Our findings show the existence of clear associations between perceived healthiness and different music features. We provide useful insights into how different musical features impact the perceived healthiness of music, how perceptions of healthiness vary between users, what trends exist between users' impressions, and what influences (or does not influence) a user's perception of healthiness in a melody. Overall, our results indicate validity in presenting health data through personalized music models. The findings can inform the design of behavior management applications on personal and ubiquitous devices. 

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5. Classifying Technologies during the Assessment, Treatment Planning, Documentation and Evaluation Phases of Music Therapy: A Survey of Board-Certified Practitioners

   https://doi.org/10.1145/3686953  

   Osorio_Torres, John; Shojaei, Fereshtehossadat; Shih, Patrick C (November 2024, Proceedings of the ACM on Human-Computer Interaction)

   Music therapists (MT-BCs) use diverse technologies to provide evidence-based personalized interventions to a wide variety of people. Most studies on the technological practices of MT-BCs report a general overview of the tools they use in their daily work. This study offers an new way of understanding technologies used by MT-BCs, classified and situated in the phases of music therapy (MT) practice: Assessment, Treatment Planning, In-Session, Documentation and Evaluation. An online survey was sent to a mailing list of 1,951 board-certified music therapists (MT-BC), and we received 104 responses. Results support distinct functions in technological practices between each of the phases of MT, revealing categories of notetaking and data entry that characterize planning, documentation and evaluation tasks, and a wider diversity of technology configurations in assessment and in-session work. We end by discussing design implications for HCI researchers and designers of music technologies for health, as well as HCI-MT design collaboration to better support the work of the MT-BC community. 

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