GRE@T-PIONEeR: teaching the nuclear data pipeline using innovative pedagogical methods

. GRE@T-PIONEeR - GR aduate E ducation A lliance for T eaching the P hys I cs and safety O f N ucl E ar R eactors - is a project funded by the Euratom – Horizon 2020 Framework Programme which aims at developing and providing specialised and advanced courses in computational and experimental reactor physics at the graduate level (MSc and PhD levels) and post-graduate level, as well as the staff members working in the nuclear industry . One of the w ork p ackage s of GRE@T-PIONEeR is devoted to developing a specific course on the nuclear data pipeline processes and to present the role of nuclear data to play in calculations of innovative reactor systems. This course covers all steps in the nuclear data life cycle, starting from the measurements to their validation and final use in nuclear reactor calculations. Beyond the technical contents of the courses being developed, the paper describes the use of innovative pedagogical methods and active learning techniques, such as flipped classes, aimed at promoting student learning.


Introduction
GRE@T-PIONEeR -GRaduate Education Alliance for Teaching the PhysIcs and safety Of NuclEar Reactors -(https://great-pioneer.eu/) is a project funded by the Euratom -Horizon 2020 Framework Programme. The project started on November 1st, 2020 for a duration of three years and gathers eight universities throughout Europe [1].
Although reactor physics has always been a core discipline in nuclear engineering, computational reactor physics relies on sophisticated models, databases and algorithms, which the engineer needs to understand, so that the tools are used most efficiently and in relevant applications. Moreover, these computational courses are often taught via advanced courses with fewer students.
The GRE@T-PIONEeR project aims at developing and providing specialised and advanced courses in computational and experimental reactor physics at the graduate level (MSc and PhD levels) and post-graduate level, as well as for staff members working in the nuclear industry. Table 1 gives a summary of the six courses offered within GRE@T-PIONEeR and the institutions involved.
The course covers all steps starting from the measurements to their validation and final use in nuclear reactor calculations, giving the students a full and comprehensive overview of: i) experiments and theory ii) compilation of information, iii) generation and evaluation of nuclear data libraries, iv) processing of nuclear data libraries for use in nuclear applications, v) assessment of nuclear data uncertainties, vi) importance of nuclear data adjustments, vii) and, finally, current activities, projects and international networks on nuclear data. To some extent, all of processes in the pipeline, including loops and feedbacks, can be seen as a "wheel", known as the "nuclear data life cycle", see Figure 1. Beyond the technical contents of the courses being developed, the novelty of the project lies with the use of innovative pedagogical methods, such as flipped classes, aimed at promoting student learning. Before attending interactive sessions organised under the close supervision of the teachers, the students have access to a handbook with the whole content of the course jointly with short videos summarising the key concepts, and online quizzes allowing testing one's understanding of those concepts.

Teaching Methodology
The pedagogical approach used and applied throughout the GRE@T-PIONEeR project essentially relies on the concept of active learning, according to which students learn much more efficiently when they participate to engaging activities in the classroom with the support from the teachers [2].
Firstly, a flipped classroom model is used with the traditional delivery of lecture-based contents moved outside of the classroom, students learn asynchronously as they can choose when and at what place to watch/read the course material.
This active learning technique relies on the idea that learning time does not always coincide with the instruction time when the students have a teacher in front of them. Therefore, pre-class activities are designed in different ways to "extend" and "adapt" the time of students to learn according to their personal situation. The on-line pre-class step is crucial for the students, they will acquire the basic concepts, knowledge about tools and databases along with useful skills.
Therefore, thanks to the flipped classroom pedagogy, the students attend the synchronous sessions much better prepared than in a traditional teaching setup.
The time spent with the teachers can be used more effectively to engage students in high order thinking activities, clarify difficult concepts and provide individual support.
Obviously, it requires more effort for the teaching staff having everything planned (materials, resources and actions embedded in pre-class activities) before teaching the subject in the synchronous sessions.
During the synchronous sessions, engaging real and practical activities are organized with the students under the teacher's supervision, thus favouring active learning. This leads to much better learning outcomes and contributes to a deeper learning.
Additionally, the teachers are able to adjust their synchronous teaching sessions to the students' needs. This can be done using the data about student learning before they attend the synchronous sessions.
These interactive sessions are offered in a hybrid format: the students can decide to attend the interactive sessions either on-site or online. The sessions are also given in a condensed format. Combined with the hybrid set-up, the courses are very well suited for lifelong learning.

The course package structure
The overall methodology for the course implementation relies on the following elements: • Handbooks covering the theoretical aspects of the covered topics. • Pre-recorded lectures (or webcasts) available for on-demand viewing and extracting the main features, results, and concepts of the handbooks. • Online quizzes associated with the webcasts, focusing on conceptual understanding, with immediate feedback to the students on their learning. • The possibility to pose questions to the teachers while watching the lectures. • Active learning sessions in forms of wrap-up and tutorials with synchronous interactions between the students and the teachers. • Use of discussion fora monitored by the teachers to maintain engagement, favour collaboration between students and for providing additional feedback and help to the students.
The entire management of all electronic resources is done via the Learning Management System (LMS) embedded in the platform SOUL (Smart Open Universe of Learning) [3].
The learning sequence for the students is made of several parts, as illustrated in Figure 2.
• Pre-class activities: The first ones include studying the handbooks, watching the webcasts, and completing the online quizzes. Interactions with the teachers are only possible in an asynchronous • Post-class activities: Finally, the students apply, analyse, and evaluate the studied concepts while solving assignments, thus using high order thinking skills. Although the biggest part of the assignments is solved during the interactive sessions, the students may be given some extra time to finalize the assignments after the sessions.

Fig. 2.
Learning sequence using both synchronous and asynchronous interactions.

WP2: teaching the ND pipeline
For the ND courses, the pre-class activities represent roughly one week of full-time studies (approx.. 40 hours). These activities are mainly the Handbooks covering the theoretical aspects of the covered topics, the pre-recorded lectures and online quizzes. There is always the possibility to pose questions to the teachers during this time.
Likewise, the in-class activities also represent one week of full-time studies. The Hands-on training sessions are split into two categories: • The first category typically corresponds to wrap-up sessions, discussions, tutorials, code usage and method implementation and are organized separately for each course module. • The second category represents hands-on training exercises organized on one of the training reactors available to the consortium (CROCUS at EPFL, Switzerland; AKR-2 at TU Dresden, Germany; BME Training Reactor at BME, Hungary). The course modules are thus worth 3 European Credit Transfer and Accumulation System (ECTS) each, where one ECTS credit is equivalent to approximately. 25 to 30 working hours.
The attendance of the hands-on activities on the training reactors gives some extra ECTS depending on the length of the corresponding sessions.

The Handbook
The Handbook covers the following topics: • Nuclear data for nuclear applications: the role of nuclear data and the necessity of better nuclear data for accurate simulations. • Some basic knowledge in nuclear physics (e.g nuclear structure and decay data, nuclear reaction cross-sections, neutron sources and fission multiplicities, energy release, fission fragments and products, photon multiplicities, etc…) One section of the Handbook addresses the neutronnucleus resonance interaction. The basic R-matrix theory is introduced, and different R-matrix formalisms used in evaluated data files are presented. • The production and use of nuclear data together with the nuclear data libraries and databases: • Generation and evaluation of nuclear data libraries: the nuclear data evaluation pipeline. Modelling versus measurements, and the Bayesian approach. A review of the GLLS technique for the evaluation. • Special purpose nuclear data evaluations libraries: Dosimetry Library and Standards Library. • The format for the evaluated nuclear data: The ENDF-6 format and the GNDS format. • Nuclear data visualization tools (e.g. JANIS, IAEA/ZZView). • Processing of nuclear data libraries for energy and non-energy applications. A review of ENSDF and ENDF analysis and utility programs. • Tool for processing nuclear reaction data (e.g. NJOY, PREPRO, FRENDY), and verification procedures to assess the quality of the processing. • Processing nuclear data in ACE format and assessing the impact in keff calculations using OpenMC code in simple Benchmarks. • Processing nuclear data uncertainties: verification and checking uncertainties. quantification. • Searching benchmarks with high sensitivity to nuclear data. • Searching similarities (e.g ck-values) between application case and Benchmarks. • Introduction of current "adjustment methodologies" to fit nuclear data and their covariances to integral data that represent the physics of the system: nuclear data libraries for specific applications.
• The compensating effects in nuclear data.
• Nuclear data adjustment methodologies (e.g. GLLS versus Bayesian Monte Carlo). In addition, the Handbook contains some simple computer-based assignments with existing open software and tools such as ENDF Utilities, NJOY, PREPRO, JANIS, DICE and NDaST.
Additional exercises in the Handbook are carried out using existing spreadsheet programs. Examples of GLLS techniques in nuclear data adjustment or nuclear data uncertainty reduction are presented using simple spreadsheets. Table 2 shows the available nuclear databases together with some visualization software, processing, and transport codes used in the course. Regarding open-web applications, there are a collection of tools developed by the OECD/NEA and the IAEA/NDS which can be extensively used through the whole nuclear data pipeline: • The NEA java applications such as JANIS for visualization of nuclear data, DICE and IDAT for nuclear data validation with criticality benchmarking, Sensitivities Viewer and NDaST to work on sensitivity analysis and uncertainty quantification, and tools developed within the framework of JEFF-3.3 evaluation: NDEC and Mapping tool.
• The IAEA/NDS visualization tools and repository of evaluated nuclear databases and other nuclear data information (e.g. ENDF, ENSDF, RIPL).
NJOY and PREPRO codes are used for the processing of evaluated nuclear reaction data libraries. To familiarize students with these tools, a few input examples are discussed and presented in the Handbook. Figure 3 gives an example to introduce students in the processing of evaluated nuclear data at different temperatures (to study the Doppler effect).  Figure 4 is an example of processing covariance data that can be used for the uncertainty propagations exercises.
The students are able to process the nuclear data libraries to feed neutron transport codes. In such cases, ACE files are generated to be used in the freely available neutron transport OpenMC code to test processed nuclear data libraries in very simple integral experiments that can be found in the ICSBEP Handbook.
The Handbook also contains a primer on basic use for different tools such as JANIS, DICE and NDaST.
Examples on how to use the capabilities implemented in these codes are also introduced with some exercises.

Hands-on exercises offered during the WPspecific synchronous sessions
The interactive synchronous sessions are based on active learning, during which the students have to implement and use the techniques they learned in handson training exercises designed to promote learning.
These hands-on computational exercises are given as one-week long sessions. The planning of the various activities during the specific synchronous sessions is shown in Table 3. Basically, the content of the hands-on exercises offered are computer-based assignments with existing open software and tools such as ENDF Utilities, NJOY, PREPRO, JANIS, DICE, NDaST and OpenMC.
Other techniques and methodologies used along the nuclear data life cycle are reviewed: GLLS, Bayesian Monte Carlo, adjustment methodologies, optimization techniques for the target uncertainty reduction problem.
To enhance the active learning during the synchronous sessions, several Guests Lecturers who are experts of the nuclear data community participate in these sessions. Thus, students have the possibility to interact and pose questions to different experts in the nuclear data pipeline. The last part of the day includes a wrap-up session where the degree of interaction between the students and teachers/experts is highest.
For the synchronous sessions, the rooms are equipped with audio and video hardware and software that allow synchronous interactions between the on-site and off-site participants in form of digital content sharing, audio interactions, and video communication. The course is restricted to a maximum of 20 students onsite and 30 students off-site.
Synchronous sessions are recorded via LMS to account for virtual students from different time zones.

Assignments and post-class activities
Finally, the students apply, analyse, and evaluate the studied concepts while solving assignments. Although the biggest part of the assignments is solved during the interactive sessions, the students may be given some extra time to finalize the assignments after the sessions. Discussion fora are also used for peer-instruction, with interventions from the teachers when necessary.
Thus, a set of assignments is used as a practical counterpart of in-class planning. Recent examples of nuclear data evaluation already discussed within the nuclear data community are presented and re-evaluated in the course. The aim of these assignments is to The selection of real and recent problems serves to engage and facilitate the learning of students instead of adopting a "passive mode" working with useless practical exercises. Thus, the class-room teaching is aligned with the needs of the nuclear data community. In this scenario, the teachers act to facilitate or guide their work. Therefore, the role of Guest Lecturers is very important as potential "influencers" who are sharing their own experience and knowledge in the field.
At the end of each day, a wrap-up session is hold where the students discuss with the teaching staff the activities carried out in the day, serving to clarify and consolidate their learning.
Finally, the simultaneous participation of students from all over the world, participating on-site and on-line in the class, helps the learning through direct and collective discussions during the working sessions.
This "work and talk" in class activities facilitates a collective creative thinking, avoiding individual activities, which can continue in post-class activities with different group of students.

Hands-on exercises on the training reactors
Additionally, GREaT-PIONEeR project has planned other interesting courses related to nuclear data: "the Hands-on exercises in training reactors". These courses addressed a set of experiments with direct application to cross-sections measurements which are carried out in research reactors: • Neutron transmission measurements and pile oscillator experiment and gold foil activation experiment on AKR-2 at the Technical University Dresden (TUD). • Reactivity worth of samples with pile oscillator (POLLEN) and gold foil activation on CROCUS at the École Polytechnique Fédérale de Lausanne (EPFL). • Cross section measurement by activation analysis (effective cross section) and measurement of the Doppler effect at the Budapest University of Technology and Economics, (BME) Training Reactor.

Conclusion
The course on "Nuclear data for energy and non-energy applications" is one of the courses developed within the framework of the EU-funded GRE@T-PIONEeR Project. First edition of this course was held in November 14-18, 2022, at the Polytechnic University of Valencia (Valencia, Spain) with a total of 50 students, 13 onsite and 37 online. A 2nd edition of this course is scheduled in September 4-8, 2023.
This course covers all steps in the nuclear data life cycle. Innovative pedagogical methods and active learning techniques such as flipped classes are used to enhance and promote student learning. Interactive sessions are offered in a hybrid format where students have the possibility to interact and pose questions to teachers and experts in the nuclear data pipeline.
At the end of the course, students have developed skills, competences and abilities to work on the nuclear data pipeline. It allows them to implement new data techniques such as AI/ML methods which have proven to be extremely helpful in studies to sieve through large amounts of heterogeneous data. Because of these course, students are able to feed such algorithms with carefully curated data and correctly interpret the results.