Integrate 1Cademy AI Assistant into Your e-Books and Course Curriculum!
DIVE INTO THE FUTURE OF EDUCATION
1Cademy AI Assistant integrates student's previous responses with the knowledge graph's prerequisite relations. Correct answers guide students toward more advanced topics, whereas incorrect answers trigger a review of the prerequisites. Students earn daily points to motivate spaced practice. The 1Cademy knowledge graph, personalized practice, and daily point incentives foster students' long-term learning, with our large controlled experiments revealing significant increase in exam scores especially amongst students with lower GPAs.
1Cademy AI Assistant provides answers exclusively based on its knowledge graph, populated with instructor-curated course content. It closely examines each student's response history, understanding prerequisite topic relationships, which aids in identifying root issues causing academic struggles. Furthermore, it visualizes prerequisite pathways to deepen students' learning. 1Cademy also offers instructors comprehensive student conversation reports, facilitating more effective teaching strategy refinement.
1Cademy empowers instructors to efficiently manage assignments and exams by offering tools to create, schedule, review, and auto-grade them, all under their control. It enables instructors to personalize questions, assign points, and tag or revise them. Students benefit from completing assignments, receiving instant constructive feedback, and reviewing grades with metacognitive learning analytics, fostering transparency in the learning process.
The 1Cademy Assistant engages learners in recognizing their progress by providing immediate feedback, celebrating correct answers, and tracking their progress throughout their academic journey. This feedback cycle, embedded at critical moments within the learning material interactions, utilizes interactive, concise, and encouraging animations illustrated by the 1Cademy Assistant character. On completing each piece of micro-content, learners receive an enhanced level of positive reinforcement through awards and reputation points, keeping the brain engaged and facilitating increased learning through consistent encouragement for small achievements.
1Cademy aims to simplify and facilitate comprehension by translating complex scientific content into easily digestible microlearning modules. Current research indicates a trend towards students' preference for micro-content delivery methods such as flashcards, over traditional lengthy text-based learning. 1Cademy actively aids teachers and students in working together to break down learning content into smaller segments of micro-content, each embodying a single concept. These bite-sized learning modules can be applied across numerous learning contexts and goals. To assist in this task, 1Cademy employs a three-way collaboration between educators, students, and its customized AI Assistant, whereby invaluable information from myriad sources is consolidated into compact, clear-cut pieces of micro-content. This diverse collaboration ensures the micro-content is relevant to a broader spectrum of students covering the same topics, extending beyond a single term of study.
For learning to be effective, the content must be progressive. 1Cademy offers a large-scale, asynchronous collaborative mechanism that allows teachers and students, with the assistance of the 1Cademy AI Assistant, to build an extensive prerequisite knowledge graph using the micro-content modules. With unique learning pathways established for each learning objective, students can seek out various prerequisite learning routes. Each pathway could better suit a different student, considering their prior knowledge base, preferred learning styles, and specific learning requirements. Once an objective is achieved, students can delve into more advanced topics, furthering their Zone of Proximal Development. This collective generation of learning pathways, under the guidance of educators, equips students with optimized mechanisms for understanding each concept.
Ensuring the quality of the knowledge graph and study pathways requires crucial oversight by educators. To assist teachers in saving their time, an AI-enhanced peer-review process has been implemented. Instructors, students, and the 1Cademy AI Assistant collaboratively evaluate each micro-content segment, and a collective score determines the need for modification or deletion. Students' upvotes help in identifying helpful content, earning the author reputation points. Conversely, downvotes indicate the need for improvement, and it leads to loss of points for the author. Unlike conventional classroom settings where students compete to acquire more knowledge, here, the competition lies in being more beneficial to the learner's community, motivating students to earn higher reputation points. It fosters a sense of accomplishment among students, giving them pride in contributing to society even as they pursue their education.
Evolving in conjunction with all stakeholders, 1Cademy notebooks are structured knowledge graphs of micro-content pieces along with their prerequisite learning pathways. Teachers and students from various institutions, who are teaching or learning the same topics, ensure that the content stays updated and improved over time. The AI Assistant also supports this continual improvement process. Multiple versions of each micro-content piece, proposed by different learners across various schools, cover an array of viewpoints and use-cases. Teachers can provide their inputs about these different versions, by acceptance, rejection, or providing suggestions for improvement. 1Cademy then visualizes these versions side-by-side, granting the learners the freedom to select the most suitable learning pathway, depending on their prerequisites, learning styles, and needs.
1Cademy has fostered the development of communities of enthusiasts for various scientific subjects, comprising individuals from diverse educational institutions and research organizations. These enthusiasts share their discoveries and insights on 1Cademy and come together on a weekly basis to delve deeper into their areas of interest. Through these interactions, we gain insight into the cutting-edge research and learning taking place at our collaborators' institutions and are able to draw connections that inspire new research ideas.
Over the past two years joined 1Cademy.
Have participated in a large-scale collaboration effort through 1Cademy
Are generated through this large-scale collaboration.
Are connected between nodes.
The traditional learning approach for students is to start with foundational concepts and gradually work towards more complex topics. On the other hand, researchers often prefer to begin with the advanced topics and work backwards to gain a deeper understanding of the underlying prerequisites. 1Cademy offers an innovative approach to learning by enabling students to emulate the research method and start with advanced topics, then delve into the prerequisites as needed. This approach allows students to learn in a more targeted and efficient manner, similar to the way researchers approach learning.
The process of meticulously considering the prerequisites for each concept when adding them to 1Cademy not only improves the quality of our learning, but also helps us uncover novel learning pathways to grasp complex concepts that we previously thought were unattainable.
1Cademy members are constantly evaluating the efficacy of the content and learning pathways. If a member discovers a more straightforward method for defining or explaining a concept, they can propose it on 1Cademy for community review. Through this process, the community collectively decides which approach is most effective for learning that particular concept. As a result, the learning experience through 1Cademy continually improves, becoming both more efficient and enjoyable over time.
While information on any topic is readily available on the internet, many people still choose to invest in textbooks and courses. The reason for this is that these resources provide structured learning pathways - step-by-step procedures to achieve one's learning objectives. However, traditional textbooks and courses are limited by the perspectives of a few authors and are infrequently updated or improved. 1Cademy offers a solution to this by providing a collaborative platform for students, instructors, and researchers to design and share learning pathways on any topic, all within the framework of a shared knowledge graph.
Similar to Wikipedia, 1Cademy is built through a collaborative effort on a large scale. However, while Wikipedia is the most comprehensive encyclopedia, 1Cademy's goal is to tap into the collective intelligence of its users to uncover the most efficient learning pathways for any given topic by identifying the most effective prerequisite connections.
Ample research in cognitive psychology has demonstrated that the act of learning with the intention of teaching others is more effective than learning for the sole purpose of being tested. On 1Cademy, we condense and depict our learning pathways with the objective of enhancing the learning experience for our collaborators. In the process, our understanding of the topics deepens as we contemplate ways to make them more accessible for others to learn.
Have you ever encountered difficulty finding relevant content to learn something, because you're not sure what the appropriate keywords are? For instance, what would you search for to learn how to create the web animations featured on a particular website? Simply searching a phrase might not yield the most helpful results. 1Cademy offers a solution to this challenge by providing both a factual search engine and a mechanism for creating a personalized view of the shared knowledge graph to facilitate exploratory search. This way, even without having the exact keywords, one can navigate through the hierarchical structure of concepts and their prerequisite links to facilitate learning.
These days, we see political, sexual, ethnic, or even scientific polarization everywhere on the Internet. Echo chambers are formed where a group of people only accept thoughts and ideas that are aligned with their perspectives, ignoring alternatives views. 1Cademy provides us with a consensus-based collaboration mechanism where alternative or even competing perspectives are placed side-by-side so that one can easily compare and contrast them to learn and rationalize each topic in different contexts.
School of Information
awarded research credits to host 1Cademy on GCP services, under award number 205607640.
Iman YeckehZaare is the founder and architect of 1Cademy. He is currently pursuing his Ph.D. at the University of Michigan, School of Information. He has a Master of Science Degree in Information Science with two specializations in Human-Computer Interaction (HCI) and Information Economics for Management (IEM) from the same institution. Additionally, Iman holds two Bachelor of Engineering Degrees in Computer Science and Information Technology.
Iman was awarded the title of Best Graduate Student Instructor of the Year 2018-2019 at the University of Michigan, School of Information. He was also a Michigan I-Corps 2013 Graduate, a Campus of the Future 2018 Semi-finalist, an Innovation in Action 2018 2nd Prize awardee, and a Learning Levers 2019 3rd Prize awardee.
Paul Resnick holds the esteemed position of Michael D. Cohen Collegiate Professor of Information, Associate Dean for Research and Innovation, and Professor of Information at the University of Michigan's School of Information. As a trailblazer in the fields of recommender systems and reputation systems, he played a pivotal role in developing the award-winning GroupLens Collaborative Filtering Recommender system, which received the 2010 ACM Software Systems Award.
In recognition of his exceptional work, Resnick received the prestigious University of Michigan Distinguished Faculty Achievement Award in 2016 and the SIGCHI CHI Academy Award in 2017. Among his numerous notable publications, "The Social Cost of Cheap Pseudonyms," co-authored with Eric Friedman, earned the inaugural ACM EC Test of Time Award. Additionally, his 2012 MIT Press book, Building Successful Online Communities: Evidence-based Social Design, co-authored with Robert Kraut, made a significant impact in the field.
In 2020, Resnick was honored as an ACM Fellow for his remarkable contributions to recommender systems, economics and computation, and online communities, an honor reserved for the top one percent of ACM Members. He served as chair of the RecSys Conference steering committee from 2013 to 2015, and in 2014, co-chaired the ICWSM Conference. Resnick obtained his Ph.D. from MIT in 1992. He has been an advisor to the 1Cademy project since 2013.
Joel Podolny, a distinguished sociologist and CEO of Honor Education, Inc., has an impressive background in academia and corporate training. Previously, he held the position of Vice President at Apple and was the founding Dean of Apple University (2009-2021), where he managed the company's internal training program for employees and executives. This program instilled a deep understanding of Apple's culture, values, and innovative mindset. Apple's online learning program, designed to provide continuous educational opportunities for all employees, has become an indispensable resource for the organization. The comprehensive curriculum covered a wide range of subjects and was crafted by an exceptional group of educators, industry practitioners, and Apple veterans.
Before working at Apple, Podolny served as Dean and Professor of Management at the Yale School of Management (2005-2008), leading a significant overhaul of the Yale MBA curriculum to better equip students for the intricate, cross-functional global landscape. Prior to his tenure at Yale, he held positions as a Professor of Business Administration and Sociology at Harvard Business School (2002-2005) and as a Professor of Organizational Behavior and Strategic Management at Stanford Graduate School of Business (1991-2002). At Stanford, he served as Senior Associate Dean and taught courses in business strategy, organizational behavior, and global management. Podolny earned his Ph.D. in Sociology from Harvard University in 1991.
Roby Harrington is currently a board member of the Camphill Foundation, an advisor to CORE ECON, a board member of governors at Stanford University Press, a special advisor to the CEO of Honor Education Technology, and a farmer at Ten Barn Farm in Ghent, NY. At W. W. Norton & Company, Inc, Roby held various positions, including sales representative (1979-82), editor of political science, philosophy, and religion (1983-2020), national sales manager (1987-93), director of the college department (1994-2020), and Vice Chairman (2007-2021). He was also the chairman of the board at Camphill Foundation (2015-2020) and a fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University (2020-2021).
Collaborative question generation and mapping by students has been shown to improve students' active (engaged) and meaningful learning. In implementing these methods into a course that enabled high levels of autonomy (as recommended by Self-determination theory), we found student procrastination to be a persistent problem. Since student contribution tends to wane over the course of a semester, the efficacy of a curriculum reliant on content generation similarly wanes. We describe our efforts in reducing procrastination in a course focused on collaborative question generation and mapping, using an iterative design research methodology, over eight months of two semesters. To encourage students to create high-quality questions, we implemented a voting system that graded students based on the number of instructor up-votes given to questions they created. While this reduced procrastination early on, students found ways to take advantage of the autonomy provided by the course curriculum, leading them to resume procrastinating. To address this issue, we adjusted the grading schema for the second semester by scoring both students' and instructors' up-votes, while also allowing students to present their created content in optional weekly meetings. The introduction of optional weekly meetings had the greatest effect towards decreasing procrastination. End-of-semester surveys reported that: 1) while students enjoyed both semesters, they gave more positive feedback for the second semester; 2) student-led discussions for personally created content, when combined with the autonomy to choose both the topic and time to study, helped their learning and time management skills.
Desirable difficulties such as retrieval practice (testing) and spacing (distributed studying) are shown to improve long-term learning. Despite their knowledge about the benefits of retrieval practice, students struggle with application. We propose a mechanism of embedding desirable difficulties in the classroom called "retrieval-based teaching." We define it as asking students many ungraded, granular questions in class. We hypothesized that this method could motivate students to (1) study more and (2) increase the spacing of their studying. We tested these two hypotheses through a quasi-experiment in an introductory programming course. We compared 684 students' granular activities with an interactive eBook between the class discussion sections where the intervention was implemented and the control discussion sections. Over four semesters, there were a total of 17 graduate student instructors (GSIs) that taught the discussion sections. Each semester, there were five discussion sections, each taught by a distinct GSI. Only one of the five per semester implemented the treatment in their discussion section(s) by dedicating most of the class time for retrieval-based teaching. Our analysis of these data collected over four consecutive semesters shows that retrieval-based teaching motivated students to space their studying over an average of 3.78 more days, but it did not significantly increase the amount they studied. Students in the treatment group earned an average of 2.36 percentage points higher in course grades. Our mediation analysis indicates that spacing was the main factor in increasing the treated students' grades.
Prior literature suggests that computer science education (CSE) was less affected by the pandemic than other disciplines. However, it is unclear how the pandemic affected the quality and quantity of students' studying in CSE. We measure the impact of the pandemic on the amount and spacing of students' studying in a large introductory computer science course. Spacing is defined as the distribution of studying over multiple sessions, which is shown to improve long-term learning. Using multiple regression models, we analyzed the total number of students' interactions with the eBook and the number of days they used it, as a proxy for studying amount and spacing, respectively. We compared two sequential winter semesters of the course, one during (Winter 2021) and one prior to the pandemic (Winter 2020). After controlling for possible confounders, the results show that students had 1,345.87 fewer eBook interactions and distributed their studying on 2.36 fewer days during the pandemic when compared to the previous semester prior to the pandemic. We also compared four semesters prior to the pandemic (Fall and Winter of 2018 and 2019) to two semesters during the pandemic (Fall 2020 and Winter 2021). We found, on average, students had 3,376.30 fewer interactions with the eBook and studied the eBook on 16.35 fewer days during the pandemic. Contrary to prior studies, our results indicate that the pandemic negatively affected the amount and spacing of studying in an introductory computer science course, which may have a negative impact on their education.
Spacing and procrastination are often thought of as opposites. It is possible, however, for a student to space their studying by doing something every day throughout the semester and still procrastinate by waiting until late in the semester to increase their amount of studying. To analyze the relationship between spacing and procrastination, we examined 674 students’ interactions with a course eBook over four semesters of an introductory programming course. We measured each student’s semester-level spacing as the number of days they interacted with the eBook, and each student’s semester-level procrastination as the average delay from the start of the semester for all their eBook interactions. Surprisingly, there was a small, yet positive, correlation between the two measures. Which, then, matters for course performance: studying over more days or studying earlier in the semester? When controlling for total amount of studying, as well as a number of academic and demographic characteristics in an SEM analysis, we find a strong positive effect of spacing but no significant effect of procrastination on final exam scores.
Extensive prior research shows that spacing – the distribution of studying over multiple sessions – significantly improves long-term learning in many disciplines. However, in computer science education, it is unclear if 1) spacing is effective in an incentivized, non-imposed setting and 2) when incentivized, female and male students space their studying differently. To investigate these research questions, we examined how students in an introductory computer science course (378 female and 310 male) spaced their studying. A retrieval practice tool in the course (for 5% of the course grade) incentivized students to space their studying, by awarding a point per day of usage. To measure how much each student spaced, we examined their interactions with the course eBook, which served as their primary learning resource. Specifically, when comparing two students with the same academic and demographic characteristics, the same measure of course easiness, and the same amount of content studied, we considered the student who distributed their studying over more days to be the one who spaced more. Using this definition, our structural equation modeling (SEM) results show that, 1) on average, students who spaced their studying over 14.516 more days (one standard deviation) got 2.25% higher final exam scores; and 2) female students spaced their studying over 4.331 more days than their male counterparts. These results suggest that, in an introductory computer science course, incentivized spacing is effective. Notably, when compared to their male counterparts, female students both exhibited more spacing and obtained higher final exam scores through spacing.
Generating multiple-choice questions is known to improve students' critical thinking and deep learning. Visualizing relationships between concepts enhances meaningful learning, students' ability to relate new concepts to previously learned concepts. We designed and deployed a collaborative learning process through which students generate multiple-choice questions and represent the prerequisite knowledge structure between questions as visual links in a shared map, using a variation of Concept Maps that we call "QMap." We conducted a four-month study with 19 undergraduate students. Students sustained voluntary contributions, creating 992 good questions, and drawing 1,255 meaningful links between the questions. Through analyzing self-reports, observations, and usage data, we report on the technical and social design features that led students to sustain their motivation.
Retrieval practice, spacing, and interleaving are known to enhance long-term learning and transfer, but reduce short-term performance. It can be difficult to get both students and instructors to use these techniques since they perceive them as impeding initial student learning. We leveraged user experience design and research techniques, including survey and participant observation, to improve the design of a practice tool during a semester of use in a large introductory Python programming course. In this paper, we describe the design features that made the tool effective for learning as well as motivating. These include requiring spacing by giving credit for each day that a student answered a minimum number of questions, adapting a spaced repetition algorithm to schedule topics rather than specific questions, providing a visual representation of the evolving schedule in order to support meta-cognition, and providing several gameful design elements. To assess effectiveness, we estimated a regression model: each hour spent using the practice tool over the course of a semester was associated with an increase in final exam grades of 1.04%, even after controlling for many potential confounds. To assess motivation, we report on the amount of practice tool use: 62 of the 193 students (32%) voluntarily used the tool more than the required 45 days. This provides evidence that the design of the tool successfully overcame the typically negative perceptions of retrieval practice, spacing, and interleaving.
In an introductory Python programming course intended for non-majors with little prior CS experience, with 85 male and 108 female students, we were able to capture electronic traces of students' studying and problem-solving. There was no significant difference in final exam scores by gender but we found that female students spent 12.1 more hours studying over the semester while male students on average earned 2.7 more points per hour of solving problem set questions over the first half of the semester. We were able to capture their learning behavior because students studied using the Runestone interactive textbook and completed weekly problem sets in the same platform for the first half of the semester. We analyzed these logs to determine three quantities for each student. One is study time, as measured by total use of Runestone outside of weekly assignments. The second is speed, as measured by the number of points students earned per hour working on problem sets. The third is earliness, as measured by how far before the deadlines they worked on weekly assignments. We conclude that male students were faster at completing problem sets early in the semester but that female students found an alternative pathway to success.
The Runestone ebook platform is open source, extensible, and already serves over 25,000 learners a day. The site currently hosts 18 free ebooks for computing courses. Instructors can create a custom course from any of the existing ebooks on the site and can have their students register for that custom course. Instructors can create assignments from the existing material in each ebook, grade assignments, and visualize student progress. Instructors can even create new content for assignments. The Runestone ebooks contain instructional material and a variety of practice problem types with immediate feedback. One of the practice types, Parsons problems, is also adaptive, which means that the difficulty of the problem is based on the learner’s performance. Learner interaction is recorded and can be analyzed. This paper presents the history of Runestone, describes the interactive features, summarizes the previous research studies, and provides detail on the recorded data. Interaction data can be shared with other learning environments through the Learning Tools Interoperability Standard (LTI).