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Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science (2018) 000–000 Procedia Computer Science 13500 (2018) 441–448
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3rd International Conference on Computer Science and Computational Intelligence 2018 3rd International Conference on Computer Science and Computational Intelligence 2018
Towards Towards an an Adaptive Adaptive Formative Formative Assessment Assessment in in Context-Aware Context-Aware Mobile Learning Mobile Learning a a Fatima Ezzahraa LOUHABa,∗ a,∗, Ayoub BAHNASSEa , Mohamed TALEAa Fatima Ezzahraa LOUHAB , Ayoub BAHNASSE , Mohamed TALEA a Lab LTI, FS Ben M’sik, University Hassan II of Casablanca, Morocco LTI, FS Ben M’sik, University Hassan II of Casablanca, Morocco
a Lab
Abstract Abstract Today and with the development of computer technologies, traditional learning, which offers a static content for all learners, is Today anddesired with theindevelopment of computerAstechnologies, learning, which offers ahas static content all methods learners, of is no longer learning environments. a result, the traditional exploitation of this development given rise tofornew no longerThe desired in learning Asmethods a result, and the exploitation of this development has givenorrise to new methods of learning. mobile learningenvironments. is one of these specifically the adaptive mobile learning context-aware mobile learning. The mobile one ofgoes these methods and stages; specifically the adaptive mobile learning context-aware learning. Generally, thelearning learningisprocess through several the assessment is part of this processor and it is a key stepmobile in the learning. Generally, the learning process goes through several stages; the assessment is part of this process and it is a keyassessment, step in the learning activity, taking several ways. When we talk about an adaptive learning, we should think also about an adaptive learning several ways. When talk about an adaptive thinkwe also about the an adaptive where theactivity, learnertaking can take an adaptive test we content according to theirlearning, context.we In should this paper, present Adaptiveassessment, Formative where the learner can take an Mobile adaptiveLearning test content according to their context. In this paper, we present the Adaptive Formative Assessment in Context-Aware (AFA-CAML) approach. The goal of this approach is to provide learners with an Assessment Context-Aware Mobile Learning taking (AFA-CAML) approach. The goal of this approach is to (Computerized provide learnersAdaptive with an adaptive andin personalized formative assessment into account the learner context based on the CAT adaptive and personalized formative assessment taking into account the learner context based on the CAT (Computerized Adaptive Tests) theory. Tests) theory. c 2018 2018 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. © c 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND This is an open access article under the CC BY-NC-ND license license (https://creativecommons.org/licenses/by-nc-nd/4.0/) (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and of of thethe 3rd3rd International Conference on Computer Science and Computational IntelSelection andpeer-review peer-reviewunder underresponsibility responsibility International Conference on Computer Science and Computational Selection and2018. peer-review under responsibility of the 3rd International Conference on Computer Science and Computational Intelligence 2018. Intelligence ligence 2018. Keywords: Mobile Learning; Adaptive Assessment; Formative Assessment; Context-Awareness. Keywords: Mobile Learning; Adaptive Assessment; Formative Assessment; Context-Awareness.
1. Introduction 1. Introduction In recent decades, the development of information technology and especially mobile technologies had a positive In recent the development of information technology and especially mobileoftechnologies a positive effect on the decades, development of mobile learning tools 11 . The latter is defined as the process learning andhad teaching that effect on the development of mobile learning tools . The latter is defined as the process of learning and teaching that occurs with the use of mobile devices offering flexible access (without the time and device constraints) to educational occurs with the use of mobile devices offering flexible access (without the time and device constraints) to educational 2 resources, experts, peers, and services from any location 2 . This development has also encouraged researchers to work development resources, experts, peers,learning and services from any 3 , 4 , 5 , has 6 on the design of mobile systems and to location study this. This methodology . also encouraged researchers to work 3, 4, 5, 6 on However, the design the of mobile learning systems and to study this methodology . challenge of mobile learning today is no longer how to make the learning objects accessible to However, the challenge of mobile learning today is no longer how to make the learning accessible to everyone anywhere, anytime, but how to adapt these objects according to the learners’ needsobjects in order to have an everyone anywhere, anytime, but how to adapt these objects according to the learners’ needs in order to have an ∗ ∗
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c 2018 The Authors. Published by Elsevier Ltd. 1877-0509 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. c 2018 1877-0509 Thearticle Authors. Published by Elsevier Ltd. This isisan under the CC licenselicense (https://creativecommons.org/licenses/by-nc-nd/4.0/) This anopen openaccess access article under the BY-NC-ND CC BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the 3rd Conference on Computer Science and Computational Intelligence 2018. Selection and peer-review under responsibility of International the 3rd International Conference on Computer Science and Computational Selection and 2018. peer-review under responsibility of the 3rd International Conference on Computer Science and Computational Intelligence 2018. Intelligence 10.1016/j.procs.2018.08.195
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effective learning. For this reason, the focus of research in mobile learning has shifted to adaptive mobile learning or context-aware mobile learning 7 , 8 , 9 . The context-awareness is the device’s ability to detect, sense, interpret, and respond to aspects of the user environment and the devices themselves 10 . Ryan et al. 11 define context-aware applications as applications that monitor sensor inputs of the environment and allow users to select contexts according to their interests and activities. Byun and Cheverst 12 give a definition that is more generally accepted: A system is contextual if it can retrieve, interpret, and use contextual information and adapt features to the current usage context. So, an adaptive or context-aware mobile learning system is a system that takes into account the different context dimensions 13 , in order to provide learners with the appropriate learning resources depending on the context. The context concept has different definitions in the literature, each one defines it according to its needs and its point of view. According to Dey 14 , context is information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and the application themselves. Schilit et al. 15 divide the context into three categories (Fig. 1); computing context (Such as connectivity, communication costs, communication bandwidth, nearby resources such as printers, displays, and workstations), user context (Such as the profile of the user, location, people nearby, and social situation), physical context (Such as lighting, noise levels, traffic conditions, and temperature).
Figure 1. Context dimensions.
When we talk about the learning process, it is mandatory to mention the assessment notion, which plays a vital role in the way of learning, in the motivation to learn, and in the learning way of teachers. The assessment provides useful information to guide teachers, to help learners reach next steps, and to monitor progress and achievements. It plays an important role in every learning and teaching activity. What is assessed and how is assessed influences the instructional process in several ways. Since assessment is part of the learning process, so in an adaptive learning system, it must, of course, have an adaptive assessment that takes into account the characteristics and specifications of the learners. The aim of this paper is to propose the Adaptive Formative Assessment in Context-Aware Mobile Learning (AFACAML) approach. The goal of this approach is to provide learners with an adaptive and personalized formative assessment taking into account the learner context. The rest of this paper is organized as follows: In the second section, we present the different assessment strategies. The third section discusses the research that has been done in adaptive assessment. The fourth section analysis the existed approaches compared to our proposed model. The fifth section introduces the proposed model of our contribution. And finally, we end with a conclusion.
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2. Assessment strategies Assessment is the process of systematically collecting, interpreting, and using the information to improve student learning and satisfaction 16 . There are many types and assessment strategies in education. The choice to use one strategy among others is based on the teacher pedagogy. 2.1. Formative assessment Formative assessment, the strategy adopted in our research, is a process that provides feedback and support during instruction, such that teachers and students can adjust ongoing instruction and learning to improve students’ achievement of planned instructional outcomes 17 . Nicol and Macfarlane-Dick 18 further interpreted formative assessment as an integral part of instruction and an important source for students and teachers to make reflections on. It can be the compass to guide students towards learning and academic achievement. Formative assessment has been recognized by educators and researchers as an important element in conducting learning activities for improving student learning effectiveness 19 . In principle, the formative assessment is carried out at the end of each learning task and is intended to inform the learner and the teacher of the degree of mastery achieved and, if necessary, to discover where and how a learner has learning difficulties, with a view to proposing or introducing him to strategies that allow him to progress. It is an assessment mode whose main objective is the regulation of the learning process. It’s basically a feedback process by providing the learner, who is the main recipient, with information about their progress towards a specific goal. It allows intervening in the learning process to influence the modalities of the action in progress, to transform the learning contents, to allow the learner to transform or to vary his methods and his strategies of learning, and decide on the necessary aid actions. It is also an assessment that allows the learner to evaluate what he knows, what he does not know, what makes him unsuccessful and, in that sense, is a path to self-assessment. 2.2. Summative assessment The primary purpose of the summative assessment is a certification goal. Indeed, it allows providing a report that places the learner in relation to an established norm or criteria, to take institutional type decisions, to obtain a diploma, to move to a higher class, for example, to locate learners in relation to one another, within one or more groups. The summative assessment, which is the sum of the learning achieved, is most often reflected in a note. The summative assessment is used as evidence of accountability on deciding if the learning was effective. It is used for grading or ranking student performance hence informing them of their overall achievement. 2.3. Diagnostic assessment The diagnostic or prognostic assessment makes it possible to situate the student in his learning. The teacher will then be able to identify his or her deficiencies even before the learning phase and the teacher will be able to check if the student knows the basics in order to possibly implement a differentiated teaching. In addition, the diagnostic assessment can also be done at the end of a cycle to guide the student. Diagnostic assessment, which occurs at the beginning of learning, is supposed to propose basic pedagogical, didactic and methodological references. It is not an assessment that leads to remediation and cognitive or procedural regulation, but an assessment that offers a global and clear vision of the reality of the class (students’ needs, gaps, potentialities,...) and which guides the initial didactic choices (elaboration of educational projects, contents definition, procedures,...). Table 1 summarizes the different aspects of each assessment strategy. Table 1. Assessment startegies. Assess
Formative assessment
Summative assessment
Diagnostic assessment
For Who ?
For the student: provides feedback on the learning achieved. For the teacher: allows regulating his teaching.
For the student. For the official sanction of studies.
For each student: effect on motivation. For the teacher: take knowledge of the achievements of his students and take into account in his teaching.
When ?
Regularly. At the end of each critical stage of learning.
At the end of an important stage of learning.
At the beginning of the session: first or second meeting.
What ?
Any element that will be subject to summative assessment must first have been evaluated in the formative mode.
Only on the goals clearly announced and pursued as a learning target for this course.
All objects likely to influence learning (conceptions, beliefs, acquired, intellectual habits).
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In the traditional learning, the content of these types of assessment is identical for all learners regardless of their level of knowledge, learning style, skills, and goals. For this reason, and especially in an adaptive learning system, as the learning activity is adapted, it is of course also necessary to adapt the content of the assessment taking into account the learners’ needs. As a result, several researchers have emerged to address the adaptive assessment issue. 3. State of the art Assessment is the biggest challenge and a driving force for the learners in mobile learning. In the adaptive assessment, the problem is the choice of questions. This problem was addressed by several researchers and the result was the Computerized Adaptive Tests (CAT). The objective of CAT is to construct an optimal test for each examinee. It uses engines to select questions according to the students’ abilities, eliminating questions that are too easy or too difficult. The questions can be multiple choice or true and false or even matching questions and tests assess student’s strengths and weaknesses on an item. With this assessment approach, students accomplish excellent results where they are proficient and identify the flaws in their learning. The SIETTE 20 is a web-based system that generally uses multiple-choice questions. The question selection is based on a function that estimates the probability of correctness to particular questions based on a temporary learner model, leading to an estimation of the students’ level of knowledge. The COMPASS 21 is also a web-based system that uses a concept map assessment tool for helping students comprehend, as well as to support the assessment process. It offers different informative and tutoring adapted feedback. The PASS 22 is a web-based assessment module, which can be integrated into an Adaptive Educational Hypermedia System (AEHS). The PASS system estimates students’ performance based on multiple assessment options. It re-estimates the difficulty level of each question at any time and relates to the importance of the educational material available. The adaptive questions approach begins with the principle that a dynamic sequence of questions depends on learners’ responses. It is supported by rules about the learner given responses and overlay the learner model to knowledge representation based on concepts. In comparison with adaptive testing, this approach presents more flexibility to teachers in order to include didactical and personal methods through the creation of appropriate rules. The CosyQTI 23 is a web-based tool for authoring and adaptive questions test based on IMS QTI, IMS LIP, and IEEE LTSC PAPI standards. It has the advantages of using standard-complaint and open information policy, which makes it interoperable and reusable in other learning environments. The iAdaptTest 24 is a desktop-based tool for adaptive question based on IMS QTI, IMS LIP, and XML Topic Maps standards. It has the possibility of reusability and interoperability of data. The LEO 25 is a software program that provides a concept-map with extra features to design a curriculum. It has a graph like structure with two types of nodes: instructional and explanation nodes that explain the topics. Romero et al. 26 have presented a test system that can be used to develop and execute adaptive and adaptable tests in both web-based and mobile devices. The system resolved the problem of authoring assessments only one single time for delivery on very different platforms. Wang 27 proposes a system to provide personalized assessment activities based on the cognitive level of the learner. The proposed model learns and memorizes good learning assessment activities for different learners, and accordingly provides a personalized learning sequence for other similar learners. A similar system was introduced in 28 where the assessment questions were triggered based on the expertise proved by the learner. Nacheva-Skopalik and Green 29 describe a different system where learning and assessment were totally adaptable based on knowledge, competencies, preferences, and needs of the learner. 4. Positioning of our contribution From the work cited in the previous section, we note that the adaptive assessment subject is an active topic for researchers. However, most of this work is mainly concerned with the learner profile to adapt the tests without taking into account the the difficulty degree of questions in order to achieve a more effective adaptation for the learner. Therefore, the purpose of this paper is to propose an adaptive formative assessment model that takes into consideration the learner’s needs and context information to adapt the assessment activity in adaptive mobile learning using the CAT theory.
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With AFA-CAML we propose a new adaptive formative assessment model that aims to provide learners with a personalized test content taking into account their context and needs in order to enhance the quality of their learning activity. 5. Adaptive Formative Assessment in Context-Aware Mobile Learning (AFA-CAML) approach The Adaptive Formative Assessment in Context-Aware Mobile Learning (AFA-CAML) approach aims to provide learners with an adapted test content based on their context and the CAT theory in context-aware mobile learning. With our approach, the learners will be able to have a personalized adaptive assessment taking into account the different context dimensions. 5.1. System architecture In this section, we describe the general architecture of our model (Fig. 2), which contains the following components:
Figure 2. Architecture of the proposed model.
• Learner: the learner connected to the system in a learning situation in order to pass his test. Once the learner arrives at the evaluation activity, he will be redirected to the interface that contains the adapted test content according to his situation. • Instructor: the instructor is the main responsible of the learning activity. He creates the tests and the learning objects and also controls the learning process of learners. • Admin: the administrator controls the back-end part of the system. He is responsible for managing the various databases and actors of the system. • Learning Objects Database: this database stores the different learning objects (courses, questions, ...) from the learning object module. • Learner Context Database: the information concerning the learner context is stored in this database by the context learner module. • Learner Profile Database: in this database we store the information related to the profile of each learner. This information is retrieved from the learner profile module.
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• Adaptive Assessment Engine: this component is at the center of our architecture, representing the most important component. It is an adaptation engine that interacts with other components to deliver the adapted test content. This engine receives adaptation information from other components (Learner Profile Module, Context Learner Module, Learning Object Module, and Adaptation Rules Modules). • Learner Profile Module: this component contains information related to the learner’s profile (such as level of knowledge, prior knowledge, preferences, objectives, learning style, skills, performances, formal education outcomes and notes, and personal information) that are stored in the learner profile database. • Context Learner Module: this module includes information about the learner context; its environment (such as time and location) and its mobile device (software and hardware). This information are detected in real-time execution by mobile sensors. • Learning Object Module: this component is responsible for the management of learning objects (courses, activities, tests,...). • Adaptation Rules Module: this component is responsible for managing the adaptation rules defined in our model. 5.2. Adaptation rules As we have already mentioned, our model is based on learning objects. These objects can be courses, activities, tests, etc. Each course is divided into a number of chapters. For each chapter, we associate a test that verifies the acquisition of the objectives of this chapter. The objectives are related to questions of different levels of difficulty (Fig. 3): • Easy (Knowledge): these questions verify the ability to remember notions already learned. • Medium (Comprehension): ability to interpret notions already learned. • Difficult (Application): ability to apply learned concepts in new contexts.
Figure 3. Learning objects structure.
This paper focuses mainly on the assessment activity which is represented by the adaptive tests associated with the course chapters. The choice of test questions is based on the learner’s profile, context, objectives related to learning objects, and the adaptation rules of our model: Rule 1: Choice of the first question In CAT’s approach, the choice of the next question is based on the answer to the previous question. As a result, the choice of the first question is problematic since there is no previous question. To solve this problem, there are two possibilities; the first one is to choose a random question of a medium level of difficulty taking also into consideration the learner context ( level of knowledge, prior knowledge, preferences, objectives, learning style, skills, performances, formal education outcomes and notes, etc). The second possibility is based on the scores of the learner in other tests to determine the first question. This possibility is not applicable in the case of a learner who interacts with the system for the first time, which means that we have no information about their previous scores. So, to overcome this
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limitation, we will adopt the first possibility. Rule 2: Choice of the next question The choice of the next question is based primarily on the answer to the first question; if this answer is correct, then the next question will be a question of a higher difficulty level. If the learner did not answer the first question correctly, then the next question will be a question of a lower level of difficulty from the questions related to the same learning objects’ objectives. Rule 3: Conditions for stopping the test The test is considered complete if the learner has answered all questions or the duration of the test has expired. So, based on these adaptation rules and the information we have about the learner and its context, learners are provided with an adaptive test content. We are using the user context to select the first question of the test content then we applied the adaptive rules for the rest of test taking into consideration the learning objects’ objectives. 6. Conclusion Assessment is an important step in the learning process. It represents the activity that makes it possible to check and control the learning process of learners. Therefore, when we talk about adaptive learning, we must, of course, think of adaptive assessment methods, in order to provide learners with an adapted and consistent learning process. The aim of this paper was to propose the Adaptive Formative Assessment in Context-Aware Mobile Learning (AFA-CAML) approach. This approach is based on the CAT theory taking into consideration the learner and its context to deliver the most adapted test content. Currently, we are working on the implementation of this model, in order to make it available for test and extract the results. References 1. Louhab, F.E., Bahnasse, A., Talea, M.. Towards a contextual mobile learning deployment: An overview. International Journal of Computer Science and Network Security 2017;17(7):80–88. 2. Sharples, M., Roschelle, J.. Guest editorial: Special issue on mobile and ubiquitous technologies for learning. IEEE Transactions on Learning Technologies 2010;3(1):4–5. 3. Harchay, A., Cheniti-Belcadhi, L., Braham, R.. A context-aware approach for personalized mobile self-assessment. Journal of Universal Computer Science 2015;21(8):1061–1085. 4. Abech, M., Da Costa, C.A., Barbosa, J.L.V., Rigo, S.J., da Rosa Righi, R.. A model for learning objects adaptation in light of mobile and context-aware computing. Personal and Ubiquitous Computing 2016;20(2):167–184. 5. Baccari, S., Mendes, F., Nicolle, C., Soualah-Alila, F., Neji, M.. Comparative study of the mobile learning architectures. In: E-Learning, E-Education, and Online Training. Springer; 2017, p. 191–200. 6. Louhab, F.E., Bahnasse, A., Talea, M.. Considering mobile device constraints and context-awareness in adaptive mobile learning for flipped classroom. Education and Information Technologies 2018;:1–26. ˇ 7. Simko, M., Barla, M., Bielikov´a, M.. Alef: A framework for adaptive web-based learning 2.0. In: Key Competencies in the Knowledge Society. Springer; 2010, p. 367–378. 8. Brusilovsky, P., Mill´an, E.. User models for adaptive hypermedia and adaptive educational systems. In: The adaptive web. Springer; 2007, p. 3–53. 9. Sampson, D.G., Zervas, P.. Context-aware adaptive and personalized mobile learning systems. In: Ubiquitous and mobile learning in the digital age. Springer; 2013, p. 3–17. 10. Hull, R., Neaves, P., Bedford-Roberts, J.. Towards situated computing. In: Wearable Computers, 1997. Digest of Papers., First International Symposium on. IEEE; 1997, p. 146–153. 11. Ryan, N., Pascoe, J., Morse, D.. Enhanced reality fieldwork: the context aware archaeological assistant. Bar International Series 1999; 750:269–274. 12. Byun, H.E., Cheverst, K.. Utilizing context history to provide dynamic adaptations. Applied Artificial Intelligence 2004;. 13. Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.. Towards a better understanding of context and context-awareness. In: International Symposium on Handheld and Ubiquitous Computing. Springer; 1999, p. 304–307. 14. Dey, A.K., Salber, D., Abowd, G.D., Futakawa, M.. Providing architectural support for context-aware applications 2000;.
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