Reconciling Adaptivity and Task Orientation in the Student Dashboard of an Intelligent Language Tutoring System (2023)

Abstract: In intelligent language tutoring systems, student dashboards should display the learning progress and performance and support the navigation through the learning content. Designing an interface that transparently offers information on students’ learning in relation to specific learning targets while linking to the overarching functional goal, that motivates and organizes the practice in current foreign language teaching, is challenging. This becomes even more difficult in systems that adaptively expose students to different learning material and individualize system interactions. If such a system is used in an ecologically valid setting of blended learning, this generates additional requirements to incorporate the needs of students and teachers for control and customizability.We present the conceptual design of a student dashboard for a task-based, user-adaptive intelligent language tutoring system intended for use in real-life English classes in secondary schools. We highlight the key challenges and spell out open questions for future research.

## introduction language learning is a complex, multidimensional process. it is therefore desirable to provide scaffolding support to learners during practice. intelligent tutoring systems (its) can implement means for this purpose in an adaptive way and provide students with insights on their progress and performance @xcite . its can accommodate individual differences through macro-adaptive exercise selection and provide micro-adaptive support while working on a selected exercise @xcite . macroadaptive systems therefore automatically determine the order in which learning content is presented, usually based on a static domain model by matching it to learner characteristics such as proficiency and learning styles @xcite . each student receives different learning material which they process at their own pace. the number of exercises a student practices is initially unknown and estimated dynamically after each exercise based on ad-hoc calculations of the student's mastery of the learning object @xcite . micro-adaptivity, on the other hand, implies that there is no static learning content. instead, the exercise contents such as hints are dynamically adjusted in order to gradually and individually guide each student towards the correct answers @xcite . thus, adaptivity improves learning outcomes by adapting to the students' individual needs @xcite . most implementations assign profiles to learners which they generate from training data. fully adaptive systems then take over all decisions, including structuring and adjusting the learning material based on the learner's profile. this can, however, inhibit them from developing their own learning strategies @xcite . enabling students to actively engage in the learning decision making process is important to facilitate self-regulation and thus can foster motivation and improve learning outcomes @xcite . self-regulation can be understood as the students' ability to organize and monitor their own learning behavior and goals by actively managing and shaping their learning environment, such as selecting the next practice target @xcite . for users to make informed decisions, it is important to show them their personal learning state, according to their interactions with the learning material. student dashboards generally aim to display information relevant to the student in order to allow them to observe and regulate their learning process. in addition, they provide means to navigate through the learning content @xcite . navigational support is especially relevant and feasible in adaptive systems that incorporate systematically generated, highly variable exercises, such as those following the implementation by @xcite . both information presentation and navigational structure should be provided in a comprehensible and accessible way. this is particularly important when tailoring a system towards young learners, who are still developing their graphical literacy skills, the ability to understand information presented in graphical form @xcite . in order to embed individualized adaptive practice with an its into real life, task-oriented language learning classrooms put an important additional focus of the student dashboard on linking practice exercises to their overarching functional goal @xcite and integrating with the curriculum the students follow @xcite . in systems used for blended learning in school settings, student dashboards must navigate through the content in a way that aligns with the curriculum, through the systems' default sequence or a sequence defined by the teacher, while maintaining enough flexibility to adjust to students' learning preferences. in addition, teachers need control over certain aspects of the learning material to satisfy the needs of teacher-guided instruction and successfully combine with the classroom-based teaching @xcite . controlling the practiced exercises to a certain extent enables them to refer to the material seen by all of their students in subsequent classroom sessions @xcite . teachers also want to be able to assign deadlines by which students need to complete practice of certain topics @xcite . since a curriculum-aligned, structured view of the entire learning content conflicts with the adaptive, dynamic content tailored to the student, it is not straightforward to combine both in a single system. we present an approach to address this challenge by supporting multiple navigational strategies and proposing metrics to display progress and performance overviews which take into account the issues faced by traditional metrics with respect to the demands imposed by adaptivity. specially tailored towards foreign language learning, our dashboard is co-designed with teachers to keep real life implications in mind and support educational practices when integrated into an intelligent language tutoring system (ilts) for the use in english classes of secondary schools in germany. ## related work an increasing number of its integrate student dashboards in form of open learner models (olm) to expose the users to their learning statistics gathered by the system @xcite . this approach has mainly been applied to higher education @xcite , thus not focusing on the particular requirements of systems used in blended learning settings of secondary school teaching. a noticeable exception constitutes the implementation by @xcite which, however, does not incorporate a task-oriented embedding of the learning content and lacks sufficient simplicity of the visualizations necessary to guide young learners in their self-regulated learning process. since most schools nowadays use task-based teaching approaches for language learning (andersen, 2019), it is necessary to further adapt student dashboards and olms to this concept. in order to represent student progress for the various skills practiced in preparation for the functional target task @xcite , the dashboard needs to make these task-essential skills explicit to students. criterion-referenced feedback, which measures performance against predefined criteria, has been successfully explored and evaluated to this purpose @xcite and later been integrated into an existing its by @xcite . their implementation is tailored towards secondary school children by making the visualizations more accessible for the target age group and incorporating task orientation into the dashboard. to this avail, they highlight the functional goal and group exercises and their performance metrics based on curricular units. this contrasts olms, which consider the learning domain as a whole @xcite . however, their system is not user-adaptive apart from providing scaffolding feedback so that the student dashboard does not consider the requirements introduced by adaptivity. integration of macro-adaptive features into a student dashboard depends on the macro-adaptive strategy the system implements. knowledge tracing (kt) approaches keep detailed learner models representing the students' progress for various skills within the practiced domain @xcite and therefore have the benefit of providing progress metrics for the skills which can be made transparent to students in the form of progress bars @xcite . they do, however, require large amounts of exercises completed by students to train the underlying model @xcite . since training data for our target group is not readily available, we cannot reliably determine precise progress values. other approaches use fixed lengths for exercise sequences with incorrect exercises repeated at the end and merely adapt the required complexity of the exercises to select (e.g., @xcite . the progress bar is then only updated when an exercise is solved correctly. while all macro-adaptive systems adaptively determine exercise sequences within a learning object, they pursue varied strategies to determine the order of learning objects. depending on the degree of self-regulation a system incorporates, it either (a) dictates the entire learning path for the topic to be practiced @xcite , (b) requires the learner to choose the next learning object themselves @xcite , or (c) provides navigation support without directly enforcing any specific order @xcite . as micro-adaptivity changes the exercise content dynamically while students work on it, assigning fixed complexity scores to exercises becomes unreasonable. macroadaptive systems therefore typically do not focus on micro-adaptive strategies, apart from providing scaffolded feedback on all exercises. the body of research on student-facing progress and performance visualizations applicable in adaptive its is growing (e.g., @xcite @xcite ). yet, most of these target higher education and thus do not consider the particular needs of teachers and students in schools. notable exceptions can be found in the domain of mathematics education (e.g., @xcite . however, to the best of our knowledge, research on student dashboards in adaptive ilts especially focusing on the demands and needs of ecologically valid k-12 second language learning classrooms is lacking. with our user-centered design we want to address this gap and offer an approach for a taskoriented student dashboard supporting different navigational strategies in a system simultaneously implementing macro-and micro-adaptivity and used for secondary school english teaching. ## dashboard design our student dashboard, illustrated in figure 1 , extends a task-oriented dashboard view so that it can be used in an adaptive ilts supporting teachers in a blended learning context. where task orientation and adaptivity requirements clash, special considerations are required. the implementation is based on the assumption that most students use the system on a tablet device in landscape mode. this assumption is backed by observations from real life classrooms. the new dashboard features have been co-designed with english teacher practitioners to ensure initial validity. following the first three stages of the latux workflow @xcite , based on a needs analysis and iterative interviews with teachers using a lowfidelity prototype, we identified requirements on adaptive ilts used in blended learning with seventh graders and created a high-fidelity prototype with mock learner data for the proposed learner dashboard. the resulting dashboard is described in the following. ## discussion since research on simultaneous integration of a task-oriented student dashboard and user-adaptivity in systems applied at secondary school level is very limited, alternative approaches can be considered in some cases and some concepts still lack empirical validation. we therefore discuss potential issues with and alternatives for some of our proposed implementations. ## conclusion we presented the design of a student dashboard for an its which integrates curriculum-driven, taskbased language teaching and user-adaptivity and has been designed in a co-participatory approach with teachers. we outlined an implementation based on practices and insights from these two instructional approaches that takes into account the opportunities, but also the requirements and restrictions of both. taking this design as starting point, we critically discussed potential limitations and alternative approaches. such conceptual and theoretical discussions will guide future work in terms of implementation and evaluation of the dashboard in authentic settings. in a next step, to pilot the design and decide on some open alternatives before fully implementing the dashboard into the system, we plan to evaluate the high-fidelity prototype in a user study with teachers to ascertain efficacy of the design. in this user study we will obtain first quantitative measures on usability and intelligibility. based on these findings, the refined student dashboard will be implemented using recent front-end development libraries like react 1 and build into the modular architecture of the ilts feedbook @xcite , connecting the dashboard with feedbook's existing micro-service landscape, including the adaptivity micro-service, the one for nlp processing and the learner model micro-service. the fully implemented dashboard integrated into the ilts will then be evaluated in a large-scale field study with student participants 1 https://react.dev/ using the system in a blended learning setting over an extended period of a school year. the data collected in that study will allow us to identify different learning paths and map them to student characteristics such as high and low self-regulation and navigational preferences such as globally adaptive, realization adaptive or completely self-guided sequencing. furthermore, the study will yield valuable insights into the practicability and acceptability of the design in real-world usage.