An official website of the United States government
This Innovate Practice full paper presents a cloud-based personalized learning lab platform. Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of understanding learner's behavior and assessing learner's performance for personalization. However, it is rarely addressed in existing research. In this paper, we propose a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment. ThoTh Lab can identify the learning style from student activities and adapt learning material accordingly. With the awareness of student learning styles, instructors are able to use techniques more suitable for the specific student, and hence, improve the speed and quality of the learning process. With that in mind, ThoTh Lab also provides student performance prediction, which allows the instructors to change the learning progress and take other measurements to help the students timely. For example, instructors may provide more detailed instructions to help slow starters, while assigning more challenging labs to those quick learners in the same class. To evaluate ThoTh Lab, we conducted an experiment and collected data from an upper-division cybersecurity class for undergraduate students at Arizona State University in the US. The results show that ThoTh Lab can identify learning style with reasonable accuracy. By leveraging the personalized lab platform for a senior level cybersecurity course, our lab-use study also shows that the presented solution improves students engagement with better understanding of lab assignments, spending more effort on hands-on projects, and thus greatly enhancing learning outcomes.
more »
« less
Construction and Analysis of Collaborative Educational Networks based on Student Concept Maps
Network Analysis has traditionally been applied to analyzing interactions among learners in online learning platforms such as discussion boards. However, there are opportunities to bring Network Analysis to bear on networks representing learners' mental models of course material, rather than learner interactions. This paper describes the construction and analysis of collaborative educational networks based on concept maps created by undergraduates. Concept mapping activities were deployed throughout two separate quarters of a large General Education (GE) course about sustainability and technology at a large university on the West Coast of the United States. A variety of Network Analysis metrics are evaluated on their ability to predict an individual learner's understanding based on that learner's contributions to a network representing the collective understanding of all learners in the course. Several of the metrics significantly correlated with learner performance, especially those that compare an individual learner's conformity to the larger group's consensus. The novel network metrics based on collective networks of learner concept maps are shown to produce stronger and more reproducible correlations with learner performance than metrics traditionally used in the literature to evaluate concept maps. This paper thus demonstrates that Network Analysis in conjunction with collective networks of concept maps can provide insights into learners' conceptual understanding of course material.
more »
« less
- Award ID(s):
- 2121572
- PAR ID:
- 10559397
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 8
- Issue:
- CSCW1
- ISSN:
- 2573-0142
- Page Range / eLocation ID:
- 1 to 22
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Individual-level interactions shape societal or economic processes, such as infectious diseases spreading, stock prices fluctuating and public opinion shifting. Understanding how the interaction of different individuals affects collective outcomes is more important than ever, as the internet and social media develop. Social networks representing individuals' influence relations play a key role in understanding the connections between individual-level interactions and societal or economic outcomes. Recent research has revealed how the topology of a social network affects collective decision-making in a community. Furthermore, the traits of individuals that determine how they process received information for making decisions also change a community's collective decisions. In this work, we develop stochastic processes to generate networks of individuals with two simple traits: Being a conformist and being an anticonformist. We introduce a novel deterministic voter model for a trait-attributed network, where the individuals make binary choices following simple deterministic rules based on their traits. We show that the simple deterministic rules can drive unpredictable fluctuations of collective decisions which eventually become periodic. We study the effects of network topology and trait distribution on the first passage time for a sequence of collective decisions showing periodicity.more » « less
-
Knowledge tracing (KT) refers to the problem of predicting future learner performance given their past performance in educational applications. Recent developments in KT using flexible deep neural network-based models excel at this task. However, these models often offer limited interpretability, thus making them insufficient for personalized learning, which requires using interpretable feedback and actionable recommendations to help learners achieve better learning outcomes. In this paper, we propose attentive knowledge tracing (AKT), which couples flexible attention-based neural network models with a series of novel, interpretable model components inspired by cognitive and psychometric models. AKT uses a novel monotonic attention mechanism that relates a learner’s future responses to assessment questions to their past responses; attention weights are computed using exponential decay and a context-aware relative distance measure, in addition to the similarity between questions. Moreover, we use the Rasch model to regularize the concept and question embeddings; these embeddings are able to capture individual differences among questions on the same concept without using an excessive number of parameters. We conduct experiments on several real-world benchmark datasets and show that AKT outperforms existing KT methods (by up to 6% in AUC in some cases) on predicting future learner responses. We also conduct several case studies and show that AKT exhibits excellent interpretability and thus has potential for automated feedback and personalization in real-world educational settings.more » « less
-
Sinatra, Anne; Goldberg, Benjamin (Ed.)Over the past decade, the educational landscape has experienced a surge of online learning and instruc-tional platforms (Liu et al., 2020). This remarkable surge can be attributed to a confluence of factors, including the rising demand for higher education opportunities, the shortage of available teaching staff, and the rapid advancements in information technology and artificial intelligence capabilities. Artificial Intelligence (AI) remained a niche area of research with limited practical applications in education for over half a century (Bhutoria, 2022; Chen et al., 2020; Roll & Wylie, 2016) from 1950 to 2010. Howev-er, in recent years, the advent of Big Data and advancements in computing power have propelled AI into the educational mainstream (Alam, 2021; Chen et al., 2020; Hwang et al., 2020). Today, the rise of machine learning, deep learning, automation, together with advances in big data analysis has sparked novel perspectives and explorations around the potential of enhancing personalized learning, a long-term educational vision of technology-enhanced course options to meet student needs (Grant & Basye, 2014). Fostering personalized learning necessitates the development of digital learning environments that dynamically adapt to individual learners' knowledge, prior experiences, and interests, while effectively and efficiently guiding them towards achieving desired learning outcomes (Spector, 2014, 2016). AI-powered technologies have made it possible to analyze data generated by learners and provide instruc-tion that matches their learning performance. Through learning analytics and data mining techniques, large datasets collected are analyzed and processed to uncover learners' unique learning characteristics, often referred to as learner profiling (Tzouveli et al., 2008). Subsequently, leveraging artificial intelli-gence algorithms, the learning content is tailored, and personalized learning paths are designed to align with each learner's identified needs and preferences, thereby facilitating personalized learning experienc-es.more » « less
-
Co-creative proccesses between people can be characterized by rich dialogue that carries each person's ideas into the collaborative space. When people co-create an artifact that is both technical and aesthetic, their dialogue reflects the interplay between these two dimensions. However, the dialogue mechanisms that express this interplay and the extent to which they are related to outcomes, such as peer satisfaction, are not well understood. This paper reports on a study of 68 high school learner dyads' textual dialogues as they create music by writing code together in a digital learning environment for musical remixing. We report on a novel dialogue taxonomy built to capture the technical and aesthetic dimensions of learners' collaborative dialogues. We identified dialogue act n-grams (sequences of length 1, 2, or 3) that are present within the corpus and discovered five significant n-gram predictors for whether a learner felt satisfied with their partner during the collaboration. The learner was more likely to report higher satisfaction with their partner when the learner frequently acknowledges their partner, exchanges positive feedback with their partner, and their partner proposes an idea and elaborates on the idea. In contrast, the learner is more likely to report lower satisfaction with their partner when the learner frequently accepts back-to-back proposals from their partner and when the partner responds to the learner's statements with positive feedback. This work advances understanding of collaborative dialogue within co-creative domains and suggests dialogue strategies that may be helpful to foster co-creativity as learners collaborate to produce a creative artifact. The findings also suggest important areas of focus for intelligent or adaptive systems that aim to support learners during the co-creative process.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3637313
