Artificial Intelligence Bringing Improvements to Adaptive Learning in Education: A Case Study
3.1. Case Study: Innovation Management and Product Development Course at PoliTo
The current situation within education demands immediate attention and a comprehensive exploration of emerging perspectives. As per the directives issued by the Board of Directors at Politecnico, an academic institution must assert control over its data analytics infrastructure, ensuring accessibility for academic purposes. Transparent governance mechanisms must be established to foster the well-being and functionality of the academic community.
While the traditional scholarly publishing infrastructure is deeply entrenched and resistant to swift change, the integration of data analytics and artificial intelligence into academia remains in its infancy, subject to ongoing evolution. Consequently, it is crucial to prevent the complete relinquishment of control over these activities to profit-driven commercial entities, which, understandably, prioritize the maximizing of returns for their stakeholders.
The proposed concept suggests a solution that is capable of generating statistical insights from historical data about students and educators, spanning multiple academic periods. This approach seeks to collect information that can aid directors, educators, and even students in making informed decisions regarding their organizational and career trajectories. The shaped data and process model architecture underwent validation using data collected from students and educators over recent years. The resulting insights empowered educators and academic leaders to make informed systemic decisions concerning the utilization of data available in information system repositories across educational institutions.
Upon completion of the requisite political and technical validation processes, at Politecnico di Torino, it was established that students have the right to access their reports on their academic performance, facilitating their self-assessment endeavors. Similarly, educators can leverage these results to evaluate their learning processes and adapt their course content in accordance with emerging classroom dynamics. Furthermore, study program administrators, committed to supporting the broader teaching community, can base their decisions on the insights derived from the same data processing procedures.
Therefore, applying analytical techniques enables educational institutions to gather, assess, and process data concerning each student in connection with their specific context, revealing how this connection influences the learning experiences of the same student. Furthermore, it facilitates the description of students’ strengths and weaknesses, thereby shaping the quality and effectiveness of their output and performance and building individual and group profiles. Given the evolving landscape of students and educators needing innovative solutions to enhance education, the adoption of this methodology enables the collection of data generated by both learners and educators. The utilization of these data, coupled with analytical models, aids in uncovering valuable insights to craft optimal responses to the educational community’s demands.
The extracted knowledge is crucial for enhancing tools and platforms that are centered around data and focused on individuals. The primary objective is to impact the educational community’s processes, which are typically formalized within the organization but are also informally represented in its perceived image.
Problem-based learning, lectures, and various specific learning methods or environments are under consideration. The primary focus of the regulatory approach is outlined as a recovery action plan, which will be devised based on data collected by the sensor. Within this context, relevant components of this investigation are marked by the bold lines on the image.
The second stage involves problem-posing, where a selection of methods and tools specific to the domain is employed to comprehensively describe the issue at hand. This phase aims to fully grasp the problem and assess its impact on the systems and the environment.
The third stage focuses on developing effective strategies to address the problem, leveraging algorithmic approaches based on insights and perspectives gathered in the previous stage, which are then suitably formalized using established practices and standards.
Moving on to the fourth stage, a minimum viable product (MVP) begins to take form. Here, hardware platforms, software components, and programming languages are combined. Rapid developing principles are applied, emphasizing the “reuse” of previously developed components to create a sustainable performance level, ultimately enhancing cost-efficiency and reducing the time taken to bring the product to the market.
The subsequent stage, encompassing deployment and dissemination, explores how marketing and communication strategies are employed through the appropriate channels to engage various stakeholders and secure funding sources.
In conclusion, from an educational perspective, the assessment is based on predefined learning objectives and outcomes for both the specific course and the Master of Science program. The perspective of the enterprise or organization plays a pivotal role in the assessment process, and self-assessment is also encouraged to compel team members to account for their contributions and estimate the costs incurred throughout the entire life cycle development process.
3.1.1. Context and Data Framework
This case study is conducted within the framework of the Innovation Management and Product Development course (GISP: PoliTo Data Lake), which currently attracts over 100 new students, making it a popular course among all offerings in Politecnico’s Master of Science programs. Students simultaneously engage in various other subjects alongside the GISP course, including project management, object-oriented programming, business planning, quality management, and data-driven application development.
The constructivist classroom in the 2023 academic year accommodates a diverse population of over 100 students and is organized weekly into two theory lessons and two teamwork-based lab sessions. In this classroom, instructors, teachers, and trainers have the role of creating a collaborative environment where learners are actively involved in their own learning. Each group, consisting of five individuals, autonomously manages their working process, enforcing internal collaboration as they tackle intricate challenges associated with specific projects. The latter are often shaped based on brainstorming sessions, sometimes with the assistance of external enterprise actors. Within each group, cooperation is pursued through various collaborative tools such as Dropbox and Google Drive for data storage, Skype, Teams, or Zoom for synchronous communication, and appropriate application and system development specification tools, such as Visio or StarUML.
The classroom layout includes multiple zones where students can convene and sit in circles, in stark contrast to the conventional teacher-centered arrangement where students sit in rows while receiving a continuous stream of lectures.
3.1.3. Course Delivery
On the one hand, it aligns with a university’s corporate-style organization, where time is systematically regulated based on labor coordination and passive interactions. On the other hand, it accommodates the demands of creativity-driven processes, primarily rooted in stimulating student engagement. An intriguing aspect of this methodology is that students actively participate in the course’s organization. They kick off the course by engaging in a meeting with the Joint Steering Committee, which was formed specifically for this purpose. This meeting serves as a platform for addressing fundamental questions that unveil various dimensions of the proposed problem.
The course spans a duration of 13 calendar weeks. The initial week focuses on introductory activities, including an overview of the course schedule and organization. The second week delves into kickoff discussions regarding the challenges that companies, or other organizations, aim to tackle. Students can also build teams during this period, bringing together complementary skills, knowledge, and experiences. The team composition is finalized after considering the introductory insights regarding the issues raised by the companies and the problem-specific needs.
Students immerse themselves in the problem-posing phase between the second and fourth weeks. Here, projects begin to take shape through a top-down deductive approach. At this stage of project life cycle management, the existing framework is recognized and serves as a foundation for developing new proposals. Questions play a pivotal role within the “problem-posing” domain, enabling a comprehensive exploration of the problem’s context. This exploration is facilitated through Lean Model Canvas (LMC), logical framework analysis (LFA), and quality functional deployment (QFD).
Moving from the fifth to the seventh week, the focus shifts to problem solving, emphasizing formal and informal specification development, often involving algorithmic techniques. Building upon the earlier problem analysis and process planning, students become proficient in using integrated computer-aided manufacturing definition for function modeling (IDEF0) and unified modeling language (UML) notation for specification processing. Their goal is to create a “to be” model, which can be compared to existing benchmarks—the “as is” state of the art.
Weeks 8 through 10 are dedicated to building a sustainable prototype that aligns with the goals and constraints established by the Joint Steering Committee.
Students engage in deployment and dissemination activities during the final three weeks (11–13th weeks). They test the prototype on an appropriate testbed and plan comprehensive communication strategies for the closing exposition, which is presented to the Joint Steering Committee. This presentation includes videos, reports, and a complete technical demonstration for the final discussion. Intermediate release dates are strategically placed to ensure the timely delivery of the LFA, QFD, and UML specifications, as well as a preliminary prototype implementation. Additionally, a well-structured timetable is established to align individual skill development.
Furthermore, an individualized self-assessment mechanism is also implemented. This mechanism allows for the differentiation of project work assessments based on each group member’s abilities and level of participation. In practice, it involves allocating a specific number of credits to each team member, who then distributes these credits among their peers based on their assessment of each colleague’s practical contributions to the prototype development.
3.1.5. Assessment Management
To gain insights into the challenges and improvements needed for course delivery, both teachers and students can express their perceptions and assess various aspects of the process using semi-quantitative scales. Educational data mining and learning analytics (EDM/LA) play a vital role in extracting hidden knowledge from educational data. These datasets often comprise data collected during course delivery periods from the university’s information system and digital learning platforms.
Educators can use tools to evaluate the course content’s structure and its effectiveness in facilitating the learning process. These tools can classify students based on their feedback and monitoring perspectives. In some instances, they can even identify regular and atypical patterns in students’ behavior, helping to pinpoint their most common mistakes and develop more effective teaching activities.
Beyond the broader domain of course management, it is essential to consider the individual student’s perspective. Both perspectives benefit from the knowledge generated through the methods described above, as teaching improvements also contribute to students’ success. In the realm of EDM/LA applications, which primarily focus on modeling behavior and evaluating students’ learning performance, various documents in the literature discuss theoretical concepts and practical implementations. These systems generate valuable feedback for both educators and students; in fact, they can detect learning behaviors and proactively flag potential issues. They follow a student-oriented approach, recommending relevant activities, resources, curriculum adjustments, or links to help foster and enhance the learning experience.
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