Adaptive cyber exercise method for students

Abstract

Adaptive cyber exercise (2) method for students (1), where the cyber exercise (2) is composed of a sequence of challenges (3), and where each student (1) is assigned an adaptation factor (αn) (4) for the completion of each challenge (3), where the adaptation factor (αn) (4) corresponding to the student (1) is updated based on the time consumed (T) (31), the attempts made (A) (32) and the hints consumed (H) (33) to solve each challenge (3) of the sequence, so that the processing and computing time is optimized as well as the time dedicated by the student to achieve it, leading to savings in computational resources and greater speed in the process.

Description

[0001] DESCRIPTION

[0002] ADAPTIVE CYBER EXERCISE METHOD FOR STUDENTS

[0003] OBJECT OF THE INVENTION

[0004] The purpose of this patent application is an adaptive cyber exercise method for students, where the cyber exercise is made up of a sequence of challenges. Each challenge has, among other attributes, the resources of maximum resolution time, maximum number of attempts and number of available hints. The aim is to dynamically adapt the resources of the maximum time and the number of attempts of the nth challenge depending on the student's performance in the previous challenge, additionally incorporating notable innovations and advantages.

[0005] BACKGROUND OF THE INVENTION

[0006] There is known interest in developing strategies to reduce computing time in electronic devices, which lead to a better user experience, a more agile resolution of processes, and a reduction in energy consumption.

[0007] More specifically, in the area of teaching, there is interest in developing adaptive learning and personalized education, which considers students' preferences, their interests, or their browsing behaviours to provide personalized content. Adaptive learning aims to meet the unique needs of each student, with the advantages shown below: i) create the opportunity to observe individual performance over time to accelerate the process of acquiring new knowledge; ii) obtain more objective and trustworthy final evaluations; iii) possibility of creating tasks taking into account the specific characteristics of each student; iv) reduction of a teacher's non-productive workload; v) increased motivation for learning; vi) formation of students' thinking styles;

[0008] Additionally, e-learning systems (or computer-based learning) that take context into account, provide content based on the characteristics of each student. The students' parameters are used to generate a sequence of activities that must be completed by them, being adapted based on their performance. To achieve this, there are different approaches to generate these adapted learning paths. The most relevant ones are listed below: a) Semantic rule model: technique that collects and monitors changes in user behaviour during the learning process to determine what adaptation techniques could be required progressively over time; b) Association rules: determine the relationships between different elements and characteristics in a population, through several phases. The first is a subjective stage to examine interest. In the second phase, the rules are classified as expected or unexpected, comparing them with other rules in the system. The third combines specific mechanisms to generate and manage a repository of rules. And in the fourth phase, recommendations are proposed and the teacher determines whether they are relevant or not; c) Student modelling with knowledge inference algorithms: a student model is generated that estimates the current knowledge they have based on the data generated, so that the following exercises can be personalized and adapted;

[0009] Thus, and in view of all the above, there is still a need to arrive at a virtual or cybernetic method of teaching exercises that optimizes processing and computing time, as well as the time dedicated by the student to achieve them. As the different challenges are focused in a personalized way, and adapted to the particular characteristics of each student, this results in savings in computational resources and greater speed in the process.

[0010] DESCRIPTION OF THE INVENTION

[0011] The present invention refers to an improvement in efficiency and computational speed in the execution of adaptive cyber exercises by students, where the cyber exercise is made up of a sequence of challenges adapted to the characteristics of each one, and to their abilities and performance in the resolution of the previous ones.

[0012] It should be mentioned that, in the present invention, a cyber exercise is built on a scenario, that is, it uses a realistic definition of a network typology with various entities correctly configured. This cyber exercise is specified with certain characteristics of it, such as the narrative, the objectives or the separate competencies. For this, the following concepts are defined:

[0013] - Identifier: unique identifier value for the cyber exercise;

[0014] - Name: title describing the cyber exercise; - Scenario: unique identifier of the scenario used by the cyber exercise;

[0015] - Narrative: descriptive text of the cyber exercise itself that details its context, presenting a narrative for the student, in a way that adds realism and makes it easier for them to know the situation of the cyber exercise;

[0016] - General objective: establishes the objective of the cyber exercise so that the student knows it;

[0017] - Competence: categorical variable that identifies the competence of the cyber exercise;

[0018] - Difficulty: ordinal categorical variable that indicates the difficulty of the cyber exercise;

[0019] - Maximum time: the maximum time in hours and minutes (HH:mm) that the student can spend trying to solve a cyber exercise. If the cyber exercise is adaptive, the final maximum time may be greater or less than this initially established maximum time;

[0020] - Sequence of challenges: represents each of the actions or sets of them that the student must perform to be able to solve the cyber exercise correctly.

[0021] On the other hand, the challenges allow the complexity of cyber exercises to be packaged into different steps or challenges, simplifying their definition and improving the potential reusability of these challenges to define various cyber exercises. Challenges can be used effectively in different cyber exercises.

[0022] In essence, cyber exercises are made up of challenges and each challenge is solvable through a hidden flag that must be achieved by the student. Achieving this flag is related to the cyber security knowledge that is necessary to solve the challenge. These challenges are defined by the following characteristics:

[0023] - Identifier: unique identifier value for the challenge;

[0024] - Name: descriptor title of the challenge;

[0025] - Score: score range that can be awarded when solving this challenge correctly, for example {10, 20, 30,... , 100};

[0026] - Difficulty: ordinal categorical variable that indicates the difficulty of the challenge, initially considered Low, Medium and High difficulty;

[0027] - Description: establishes the specific objective of the challenge so that the student knows it;

[0028] - Competence: categorical variable that identifies the competence necessary to solve the challenge.

[0029] - Standard Solution: a description of the necessary steps that the student must follow to solve the challenge correctly using the solution designed by the creator / instructor. - Flag: a token that must be inserted by the student and that allows knowing that the student has satisfactorily solved the challenge;

[0030] - Hints: a series of hints related to the resolution of the challenge, where each hint has a description and a penalty in points if requested by the student;

[0031] - Maximum time: the maximum time in hours and minutes (HH:mm) that the student can spend trying to solve this challenge. If the cyber exercise is adaptive, the maximum time assigned to a challenge may be greater or less than this initially established maximum time;

[0032] - Number of attempts: maximum number of flag attempts allowed;

[0033] Both the cyber exercises and the challenges share the variables of difficulty, competence and maximum time, with the value of these variables in the cyber exercises calculated automatically by aggregating the values of the challenges that compose them. Thus, the objective of the adaptive system is to adapt the cyber exercises to the previous performance of each student. This adaptation is carried out at the challenge level, using an adaptation factor (an) that will be updated between challenges and that will also allow memory of the student's performance in different cyber exercises. Since a cyber exercise is made up of a sequence of challenges, this offers an optimal situation to be able to adapt the difficulty of the challenges by varying the resources available for each of them.

[0034] Thus, each cyber exercise is made up of a sequence of consecutive challenges, which offers the possibility of adapting the difficulty of the challenges by varying the resources available for that challenge. The available resources that we identify to solve the cyber exercises are the maximum time (T), the maximum number of attempts (A), and the available hints (H). In summary, the student solves challenge X with available resources adapted to his previous performance, which generates performance measures in challenge X, that together with their adaptation factor (an), will be introduced to the adaptive model, in order to adapt all the challenges to the particular performance of the student.

[0035] It is worth mentioning the considerable savings in processing time associated, using the adaptive system, with respect to the time needed without using the adaptive system. Specifically, the time used by any two students when the adaptive system is not used, compared to the time used by a “smart” student and a “retarded” student who do use the adaptive system, ranging between 20% and 30%, on average depending on the values of gamma y. More particularly, the adaptive cyber exercise method for students, where the cyber exercise is composed of a sequence of challenges, and where each student is assigned an adaptation factor (an) for the completion of each challenge, includes an adaptation factor (an) corresponding to the student that is updated based on the time consumed (T), the attempts made (A) and the hints consumed (H) to solve each challenge in the sequence. In this way, the cyber exercise is more personalized to the individual user, implying greater processing efficiency and a reduction in computing time, with faster resolution and obtaining results, and energy savings. The adaptation will happen in the challenge (n+1) based on what was done in the current challenge (n).

[0036] Additionally, the adaptive cyber exercise method for students includes a relevance factor (WT) of the time consumed (T) between 0 and 1 , a relevance factor (WA) of the attempts made (A) between 0 and 1, and a relevance factor (WH) of the hints consumed (H) between 0 and 1, where the sum of the three factors (WT, WA and WH) is equal to 1, according to the formula:

[0037] WT+ WA+ WH= 1

[0038] In this way, the necessary processing is reduced given that, through this formula, the instructor can direct the behaviour of the students towards the desired behaviour, making it necessary to execute a smaller number of cyber exercises. For example, by increasing the relevance of the consumption of hints, it is possible to encourage students who are very dependent on help to soon increase their autonomy. And achieving this autonomy without the adaptive system would have required a greater number of cyber exercises. Therefore, this formula allows reducing the consumption of computational resources and the time consumed, both for students and instructors.

[0039] Preferably the relevance factor (WT) of the time consumed (T), the relevance factor (WA) of the attempts made (A), and the relevance factor (WH) of the hints consumed (H) are equal to each other and to the value of 1 / 3, so that the same importance is given to all resources of time consumed WT, attempts made WA and hints consumed WH, according to the formula: wT= wA= wH= |

[0040] Note that with these particular values they would correspond to the default values in which it is necessary to simultaneously enhance the student's time efficiency, autonomy and precision. In this situation, the educational experience of said student is optimized to achieve these three objectives simultaneously, which again means the execution of a smaller number of cyber exercises, thus increasing the effectiveness and efficiency of the platform, implying less energy consumption.

[0041] According to another aspect of the invention, once a challenge (n) has been completed, within a sequence of challenges of a cyber exercise, given the time consumed (Tn), the score (PT") for said time consumed (Tn), it is defined by the formula PTn=WT*(1-Tn / (Tmaxn)), or otherwise:

[0042] Using this formula, which evaluates the time consumed by the student in the challenge, the adaptive system can adjust the resources of the challenges taking into account the time needed by each student. In this way, the adaptive system detects students who solve cyber exercises long before the maximum time, so the resources assigned to these students are reduced, including the maximum time available. Thanks to this, the educational effectiveness of the cyber exercise platform is improved, reducing the amount of processing necessary and its energy consumption.

[0043] On the other hand, once a challenge (n) has been completed, within a sequence of challenges of a cyber exercise, given the attempts made (An), the score (PA") for said attempts made (An), is defined by the formula PAn=WAx(1-(An-1) / (Amaxn-1)), or otherwise:

[0044] This formula evaluates the consumption of the number of solution attempts. Thus, the adaptive system takes into account those students who need a high number of attempts to solve the challenges of a cyber exercise. Because the adaptive system takes into account and adapts the available attempts, a student who needed many attempts will receive the attempts he needs for an optimal educational experience, but no more. And the student who needs a smaller number of attempts will receive a figure adjusted to his level, and no more, unnecessarily. This ultimately reduces the number of cyber exercises that students need, and hence the consumption of resources and processing that they entail. Additionally, once a challenge (n) has been completed, within a sequence of challenges of a cyber exercise, given the hints consumed (Hn), the score (PH") for said hints consumed (Hn), is defined by the formula PHn=WHx(1-H (Hmaxn)), or otherwise:

[0045] This formula takes into account the student's consumption of hints, which allows the adaptive system to detect those students who need more help to solve the challenges. In this way, the adaptive system knows which students need additional help and therefore provides them with the exact resources they need to complete the cyber exercises. When considering the consumption of hints, the adaptive system takes into account the help that some students need during the completion of the cyber exercises, adjusting it to their specific level, so that the number of processes is reduced, and therefore the energy consumption of the computer platform where these are carried out.

[0046] In a preferred embodiment of the invention, the final normalized score (PnOrmn) between the resources of the time consumed (Tn), the attempts made (An) and the hints consumed (Hn), are defined by the formula Pnormn=Cnx(PTn+Pn+PHn), where the variable Cnhas the value 0 if the challenge (n) was not solved correctly, and 1 if it was solved correctly, so that PnOrmnwill also take the value 0 when it has not reached any point in the challenge, and the value 1 when it reaches the maximum score. The formula otherwise is: pn > rnv( pn i pn , pn ‘ norm —c AV T TrA ' ‘ H J

[0047] This formula allows us to consider whether the student has solved the challenge or not, while evaluating the consumption of hints, solution attempts and time. Thanks to parameter C, the adaptive system can help those students who cannot solve the challenge, even if they barely consume hints and solution attempts. And these students are provided with more resources, especially time, so that they can complete the different challenges. Otherwise their educational experience would be incomplete, and to reach the required level of training they would need to perform a much greater number of cyber exercises than necessary, consuming more energy resources. Therefore, the use of this formula leads to a reduction in the total processing necessary, and less wear on the computer platform that executes it. Additionally, the adaptation factor (an+1) is defined by the formula an+1=an+Yx(1 / 2-Pnormn), or in another way:

[0048] This formula allows the adaptive system to have memory between different challenges. In this way, the system is more precise since, instead of taking into account specific performance in a challenge, it takes into account the accumulated performance in all the challenges of the cyber exercise, and even the history of past cyber exercises. By having a longer history, the adaptive system is more effective, requiring the execution of a smaller number of cyber exercises to achieve the desired educational objectives. This means savings in energy resources of the computer platform where the cyber exercises are executed.

[0049] In this way, an adaptation factor (an) is used that will be updated between challenges and that will also allow memory of the student's performance in different cyber exercises.

[0050] It is worth mentioning that the time consumed (T) available for the challenge (n+1) is equal to the multiplication of the adaptation factor (an+1) by the time consumed (T) available for the initial challenge of the cyber exercise sequence.

[0051] This formula allows adjusting the time of the next challenge based on the performance of each student. In this way, each student will have the time they need to solve the challenge, guaranteeing a complete educational experience and optimizing energy resources and the amount of processing to be carried out by the computer platform where the cyber exercises are carried out.

[0052] On the other hand, the attempts made (A) available for the challenge (n+1) is equal to the multiplication of the adaptation factor (an+1) by the attempts made (A) available for the initial challenge of the cyber exercise sequence.

[0053] Through this calculation, the solution attempts available to each student are additionally adapted based on their performance. This allows additional attempts to be provided to students who need them, but not unnecessarily to those who do not need them, ultimately reducing the number of cyber exercises needed to achieve the desired training objectives. As a result, the use of energy resources is reduced, and the resources of the computer platform where the cyber exercises are carried out are optimized.

[0054] Also the object of the present invention is an electronic device configured to execute the adaptive cyber exercise method as set forth above. An electronic device can be understood as devices commonly used by students such as, but not limited to, a computer, a tablet or a smartphone.

[0055] The attached drawings show, by way of non-limiting example, an adaptive cyber exercise method for students, constituted in accordance with the invention. Other characteristics and advantages of said adaptive cyber exercise method for students, object of the present invention, will be evident from the description of a preferred, but not exclusive, embodiment, which is illustrated by way of non-limiting example in the accompanying drawings.

[0056] BRIEF DESCRIPTION OF THE DRAWINGS

[0057] To complement the description that is being made and in order to help a better understanding of the characteristics of the invention, a set of drawings is attached as an integral part of said description, where, with an illustrative and non-limiting nature, the following has been represented:

[0058] Figure 1- View of an example of architecture for the continuous improvement of the contents of a course, in accordance with the present invention;

[0059] Figure 2- View of an example of architecture for adaptive learning, according to the present invention;

[0060] Figure 3- View of a diagram of the design of the adaptive system and its components, according to the present invention;

[0061] Figure 4- View of a model of rules for the adaptation of challenges, according to the present invention;

[0062] Figure 5- View of a comparative graph between various students and an adaptive system for a low gamma value, according to the present invention;

[0063] Figure 6- View of a comparative graph between various students and an adaptive system for a high gamma value, according to the present invention; DESCRIPTION OF A PREFERRED EMBODIMENT

[0064] In view of the aforementioned figures and, in accordance with the numbering adopted, an example of a preferred embodiment of the invention can be observed, comprising the parts and elements indicated and described in detail below.

[0065] It must be specified that the invention is originally defined to be used in the context of a Cyber Range, where each cyber exercise (2) is defined by a number of individual challenges (3), and each of these challenges (3) has some resources available to be completed. In the context of this invention, we have considered such available resources as the time available, or time consumed (T) (31), to solve a challenge (3), the number of hints available, then hints consumed (H) (33) , and the number of attempts available, then attempts made (A) (32).

[0066] In Figure 1 you can see a view of an example of architecture for the continuous improvement of the contents of a course. On the other hand, in Figure 2 you can see a view of an example of architecture for adaptive learning for a student (1).

[0067] In Figure 3 you can see a view of a diagram of the design of the adaptive system and its components, with a student (1) ready to perform a cyber exercise (2) made up of a sequence of challenges (3), which include the variables time consumed (T) (31), attempts made (A) (32) and hints consumed (H) (33), weighted by an adaptation factor (an) (4).

[0068] Figure 3 shows a global vision of how the system is integrated into a Cyber Range and the definition of the adaptive model. In this context of the invention, it is possible that in other Cyber Ranges the invention could also be applied if there were fewer types of resources available or more types of resources available. Furthermore, this invention of the adaptive system would also be applicable in other interactive learning contexts that were not necessarily a Cyber Range. For example, in the context of interactive intelligent tutoring tools, the available resources of time consumed (T) (31), attempts made (A) (32) and hints consumed (H) (33) could also be modelled in each exercise, and therefore the adaptive model could be applied. Consequently, although the preferred context of invention is that of Cyber Ranges, it would also be applicable in other interactive learning contexts where the activities that students (1) must solve have available resources that are adaptable. In more detail, note that, as shown in Figure 3, with a diagram of the design of the adaptive system and its components, on the one hand, there are the instructors, who will configure the cyber exercises (2) that the students (1) must solve. On the other hand are the students (1), who are going to solve these cyber exercises (2). Each of the cyber exercises (2) is made up of a series of sequential challenges (3), which in turn have a base of resources available to be solved, which are also configured by the instructors. The available resources that we identify to solve the cyber exercises (2) are the maximum time (T), then time consumed (T) (31), the maximum number of attempts (A), then attempts made (A) (32), and the available hints (H), then hints consumed (H) (33). In the event that student (1) exceeds the maximum number of attempts available or the maximum time, said student (1) will no longer be able to solve challenge (3) correctly.

[0069] The student (1) performs his first challenge (3) on the platform with the base resources configured by the instructor since initially his adaptation factor (an) (4) takes the default value of 1. Once the student (1) completes that first challenge (3), the associated performance measures are generated, and these are introduced into the adaptive model, which will be responsible for updating the adaptation factor (an) (4) of the student (1) and adapting the available resources with respect to their original base established by the instructor. Again, the student (1) will solve challenge (3) 2 with available resources adapted to his previous performance, will generate performance measures in challenge (3) 2, which together with his adaptation factor (an) (4) will be introduced to the adaptive model. The latter will update the adaptation factor (an) (4) and adapt the resources available for challenge (3) 3. In the same way all challenges (3) will be adapted to the student's performance (1).

[0070] The objective behind this adaptive system, which may or may not be enabled in a Cyber Range, is that when it is active, the students (1) who are performing the cyber exercise (2), will have different resources available based on their performance. This can be an interesting option in some situations where you want to reinforce the students' (1) motivation, but it could be an undesirable alternative in those situations where you want all students (1) to have exactly the same resources to solve a cyber exercise (2), so that this makes it possible to better compare the performance of one student (1) with another, as well as the overall progress of an entire group of students (1). The implementation as an adaptive model of a model-based system provides the necessary flexibility by defining heuristics, without the need to have large amounts of data. In Figure 4 you can see a view of a model of rules for the adaptation of the challenges (3), detailing the resources of the time consumed (T) (31), the attempts made (A) (32), and the hints consumed (H) (33), with the intervention of an adaptation factor (an) (4).

[0071] More specifically, it should be noted that Figure 4 shows an adaptive model designed in which a cyber exercise (2) begins with challenge (3) #1, or first challenge (3) of a cyber exercise (2), which only takes into account the adaptation factor (an) (4) of the student (1) and the available resources. Once the student (1) solves it, his performance measures are analyzed, on which we now consider the time consumed (T) (31) to solve said challenge (3), the attempts made (A) (32), and the hints consumed (H) (33). According to these performance measures and the adaptation factor (an) (4) of the student (1), the adaptation factor (an) (4) of said student (1) and the available resources are increased or decreased. The model is designed in an extensible way to be able to easily add new performance measures, such as stress. If the time, attempts and hints have been low and the student (1) solves the exercise correctly, the model understands that the difficulty has been low for this student (1). Therefore, the adaptive system will decrease the adaptation factor (an) (4) and the proportion of resources available in challenge (3) #2 will be reduced compared to challenge (3) #1. If, on the other hand, the student (1) has invested a lot of time, attempts and hints, or has not solved the exercise correctly, it is understood that the challenge (3) has been complicated for the student (1), and therefore, its adaptation factor (an) (4) will increase and the proportion of resources available in challenge (3) #2 will be greater than in challenge (3) #1. The middle option is for the student (1) to solve challenge (3) with an average amount of time, attempts and hints, so it is understood that the difficulty is adequate, and in challenge (3) #2 the factor adaptation (an) (4) will barely vary, causing the proportion of assigned resources with respect to those established by the instructor to be similar to that of the previous challenge (3). This adaptation will be carried out in the same way in each of the challenges (3) of a cyber exercise (2) with the exception of the first challenge (3). As previously mentioned, the first challenge (3) of each cyber exercise (2) is adapted considering only the initial available resources and the adaptation factor (an) (4) of the student (1).

[0072] In figure 5 you can see a view of a comparative graph between various students (1) and an adaptive system for a low value of gamma y, while in figure 6 you can see a view of a comparative graph between various students (1) and an adaptive system for a high value of gamma y. It must be specified that said figures 5 and 6 establish a comparison between graphs, for different values of gamma, with the maximum time granted to a student (1) who does not use the adaptive system, in relation to a smart student (1) and a retarded one that does use the adaptive system. On the other hand, we see the time actually used by any student (1), who does not use the adaptive system, in relation to a smart student (1) and a delayed one who do use the adaptive system.

[0073] As can be seen, the lower the value of gamma y, the more the three graphs converge. And, on the contrary, the greater the value of gamma y, the more the graphs diverge, because there is greater adaptation. It is also observed how the retarded student (1) begins consuming a large amount of time in the challenges (3) and therefore the adaptive system assigns him a maximum time greater than the default. As the challenges (3) pass, thanks to the fact that the adaptive system assigned more resources, he manages to learn and the amount of resources he needs to solve the challenges is reduced. The conclusion of these graphs is that there is a saving in computational resources both in a smart student (1) and in a retarded one. In the case of the smart one because he would have an excess of resources, and in the case of the retarded one because thanks to the adaptive system he learns, and do not need to consume so many resources.

[0074] More particularly, as seen in Figures 3 and 4, the invention refers to an adaptive cyber exercise (2) method for students (1), where the cyber exercise (2) is composed of a sequence of challenges (3), and where each student (1) is assigned an adaptation factor (an) (4) to carry out each challenge (3), where the adaptation factor (an) (4) corresponding to the student (1) is updated based on the time consumed (T) (31), the attempts made (A) (32) and the hints consumed (H) (33) to solve each challenge (3) in the sequence.

[0075] Specifically, a cyber exercise (2) begins with challenge (3) 1 that only takes into account the adaptation factor (an) (4) of the student (1) and the available resources. Once the student (1) solves it, his performance measures are analyzed, on which we now consider the time consumed (T) (31) to solve said challenge (3), the attempts made (A) (32) and the hints consumed (H) (33). According to these performance measures, the adaptation factor (an) (4) of the student (1) increases or decreases, and hence the available resources.

[0076] Preferably, as seen in Figures 2 and 3, the adaptive cyber exercise (2) method for students (1), comprises a relevance factor (WT) of the time consumed (T) (31) between 0 and 1 , a relevance factor (WA) of the attempts made (A) (32) between 0 and 1 , and a relevance factor (WH) of the hints consumed (H) (33) between 0 and 1 , where the sum of the three factors (WT, W and WH) is equal to 1 , or:

[0077] WT+ WA+ WH= 1

[0078] Additionally, as seen in Figures 2 and 3, the relevance factor (WT) of the time consumed (T) (31), the relevance factor (WA) of the attempts made (A) (32), and the relevance factor (WH) of the consumed hints (H) (33) are equal to each other and to the value of 1 / 3, so that the same importance is given to all resources of time consumed WT, attempts made WA and hints consumed WH. That is to say wT= wA= wH= |

[0079] On the other hand, as seen in Figures 3 and 4, once a challenge (3) (n) has been completed, within a sequence of challenges (3) of a cyber exercise (2), given the time consumed (Tn), the score (PTn) for said time consumed (Tn), is defined by the formula PTn=WTx(1-T7(Tmaxn)), or

[0080] On the other hand, as seen in Figures 3 and 5, once a challenge (3) (n) has been completed, within a sequence of challenges (3) of a cyber exercise (2), given the attempts made (An), the score (PAn) for said attempts made (An), is defined by the formula PAn=WAx(1-(An- 1) / (Amaxn-1)), or

[0081] Additionally, as seen in Figures 3 and 4, once a challenge (3) (n) has been completed, within a sequence of challenges (3) of a cyber exercise (2), given the hints consumed (Hn), the score (PHn) for said hints consumed (Hn), is defined by the formula PHn=WHx(1-H (Hmaxn)), or According to another aspect of the invention, as seen in Figures 3 and 4, the final normalized score (Pnormn) between the resources of the time consumed (Tn), the attempts made (An) and the hints consumed (Hn), It is defined by the formula Pnormn=Cnx(PTn+Pn+PHn), where the variable Cn has the value 0 if challenge (3) (n) was not solved correctly and 1 if it was solved correctly. pn > rnv(pn i pn i pn "norm—M T ‘

[0082] Additionally, as seen in Figures 3 and 4, the adaptation factor (an+1) (4) is defined by the formula an+1=an+Yx(1 / 2-Pnormn), or

[0083] Preferably, as seen in Figure 3, the time consumed (T) (31) available for challenge (n+1) (3) is equal to the multiplication of the adaptation factor (an+1) (4) by the time consumed (T) (31) available for the initial challenge (3) of the cyber exercise sequence (2).

[0084] In more detail, as seen in Figure 3, the attempts made (A) (32) available for challenge (n+1) (3) is equal to the multiplication of the adaptation factor (an+1) (4) by the attempts made (A) (32) available for the initial challenge (3) of the cyber exercise sequence (2).

[0085] Also the object of the invention, as seen in Figure 3, is an electronic device configured to execute the adaptive cyber exercise (2) method as described above.

[0086] The details, shapes, dimensions and other accessory elements, as well as the components used in the implementation of the adaptive cyber exercise (2) method for students (1), may be conveniently replaced by others that are technically equivalent, and do not deviate from the essentiality of the invention nor the scope defined by the claims that follow the following list. List of numerical references:

[0087] 1 student

[0088] 2 cyber exercise

[0089] 3 challenge

[0090] 31 time consumed (T)

[0091] 32 attempts made (A)

[0092] 33 hints consumed (H)

[0093] 4 adaptation factor (an)

[0094] Texts in figures:

[0095] F11 Data

[0096] F12 Creation of association rules

[0097] F13 Subjective analysis

[0098] F14 Creating a knowledge base

[0099] F15 Recommendations

[0100] F16 Course

[0101] F21 Student: i) Learning style; ii) Personal profile; iii) Competencies of the user; iv) Perceptual abilities and reasoning skills

[0102] F22 Inference model of the student preferences

[0103] F23 To prefer learning paths of the authors

[0104] F24 Generate a new learning path

[0105] F25 Simple model

[0106] F26 Learning path identified by the author

[0107] F27 Reasoning model based in cases

[0108] F28 Features preferences + Learning path + User feedback

[0109] F29 Repository model of learning objects: Metadata + Visual components + Audio components + Kinesthetic components

[0110] F31 Cyber range

[0111] F32 Adaptive system

[0112] F33 Performance measures

[0113] F34 Adaptive model F35 Cyber exercises

[0114] F36 Student

[0115] F37 Instructor

[0116] F38 Available resources

[0117] F41 Adaptive system

[0118] F42 Performance measures Challenge #i

[0119] F43 Adapted resources Challenge #i+1

[0120] F44 Initial resources Challenge #i+1

[0121] F51 Maximum time smart student

[0122] F52 Maximum time retarded student

[0123] F53 Maximum time without adaptation F61 Maximum time smart student

[0124] F62 Maximum time retarded student

[0125] F63 Maximum time without adaptation

Claims

CLAIMS1- Adaptive cyber exercise (2) method for students (1), where the cyber exercise (2) is made up of a sequence of challenges (3), and where each student (1) is assigned an adaptation factor (an) (4) for the completion of each challenge (3), characterized in that the adaptation factor (an) (4) corresponding to the student (1) is updated based on the time consumed (T) (31), the attempts made (A) (32) and the hints consumed (H) (33) to solve each challenge (3) in the sequence.2- Adaptive cyber exercise (2) method for students (1), according to claim 1, characterized in that it comprises a relevance factor (WT) of the time consumed (T) (31) between 0 and 1, a relevance factor (WA) of the attempts made (A) (32) between 0 and 1 , and a relevance factor (WH) of the hints consumed (H) (33) between 0 and 1 , where the sum of the three factors (WT, WA and WH) is equal to 1.3- Adaptive cyber exercise (2) method for students (1), according to claim 2, characterized in that the relevance factor (WT) of the time consumed (T) (31), the relevance factor (WA) of the attempts performed (A) (32), and the relevance factor (WH) of the consumed tracks (H) (33) are equal to each other and to the value of 1 / 3.4- Adaptive cyber exercise (2) method for students (1), according to any of the previous claims, characterized in that once a challenge (3) (n) has been completed, within a sequence of challenges (3) of a cyber exercise (2), given the time consumed (Tn), the score (PT") for said time consumed (Tn), is defined by the formula:PTn=wTx(1-Tn / (Tmaxn))5- Adaptive cyber exercise (2) method for students (1), according to claim 4, characterized in that once a challenge (3) (n) has been completed, within a sequence of challenges (3) of a cyber exercise (2), given the attempts made (An), the score (PA") for said attempts made (An), is defined by the formula: PAn=WAx(1-(An-1) / (Amaxn-1))6- Adaptive cyber exercise (2) method for students (1), according to claim 5, characterized in that once a challenge (3) (n) has been completed, within a sequence of challenges (3) of a cyber exercise (2), given the hints consumed (Hn), the score (PHn) for said hints consumed (Hn), is defined by the formula: PHn=WHx(1-H (Hmaxn))7- Adaptive cyber exercise (2) method for students (1), according to claim 6, characterized in that the final normalized score (PnOrmn) between the resources of the time consumed (Tn), the attempts made (An) and the hints consumed (Hn), is defined by the formula Pnormn=Cnx(PTn+PAn+PHn), where the variable Cnhas the value 0 if the challenge (3) (n) was not solved correctly and 1 if it was solved correctly.8- Adaptive cyber exercise (2) method for students (1), according to claim 7, characterized in that the adaptation factor (an+1) (4) is defined by the formula:n+1=an+Yx(l / 2-PnOrmn)9- Adaptive cyber exercise (2) method for students (1), according to claim 8, characterized in that the time consumed (T) (31) available for the challenge (3) (n+1) is equal to the multiplication of the adaptation factor (an+1) (4) by the time consumed (T) (31) available for the initial challenge (3) of the cyber exercise (2) sequence.10- Adaptive cyber exercise (2) method for students (1), according to any of claims 8 or 9, characterized in that the attempts made (A) (32) available for the challenge (3) (n+1) are equal to the multiplication of the adaptation factor (an+1) (an+1) (4) by the attempts made (A) (32) available for the initial challenge (3) of the cyber exercise (2) sequence.11- Electronic device configured to execute the adaptive cyber exercise (2) method according to any of the previous claims.

Images