Reconciling Adaptivity and Task Orientation in the Student Dashboard of an Intelligent Language Tutoring System
Year: 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.

heading: 1 Introduction
text: 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 (Phobun and Vicheanpanya, 2010).
ITS can accommodate individual differences through macro-adaptive exercise selection and provide micro-adaptive support while working on a selected exercise (Slavuj et al., 2017). 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 (Hafidi and Bensebaa, 2014). 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 (Rus et al., 2014). 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 (Lim et al., 2023). Thus, adaptivity improves learning outcomes by adapting to the students’ individual needs (Phobun and Vicheanpanya, 2010). 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 (Howell et al., 2018). 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 (Lim et al., 2023). 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 (Schunk and Zimmerman, 2013).
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 (Bull and Kay, 2010). Navigational support is especially relevant and feasible in adaptive systems that incorporate systematically generated, highly variable exercises, such as those following the implementation by Heck and
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Meurers (2022). 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 (Roberts and Brugar, 2017).
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 (Andersen, 2019) and integrating with the curriculum the students follow (Phillips et al., 2020).
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 (Burstein et al., 2012). 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 (Feng et al., 2014). Teachers also want to be able to assign deadlines by which students need to complete practice of certain topics (Hertz, 1992).
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.

heading: 2 Related work
text: 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 (Bull et al., 2016). This approach has mainly been applied to higher education (Schwendimann et al., 2017), 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 Rudzewitz et al. (2019) 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 (Ellis, 2016; Mislevy et al., 2002), 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 (Mirmakhmudova, 2021; Alawar and Abu-Naser, 2017) and later been integrated into an existing ITS by Colling et al. (2022). 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 (Bull and Kay, 2010). 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 (Liu et al., 2021) 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
(Effenberger, 2018). They do, however, require large amounts of exercises completed by students to train the underlying model (Chen et al., 2018). 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., Musa and Mohamad, 2017). 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 (Brusilovsky, 1992), (b) requires the learner to choose the next learning object themselves (Twigg, 2003), or (c) provides navigation support without directly enforcing any specific order (Brusilovsky, 2000). 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., Xia et al., 2019; Loboda et al., 2014; Bull and Kay, 2007). 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., Long and Aleven, 2017). 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.

heading: 3 Dashboard design
text: 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 (MartinezMaldonado et al., 2015), 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.
Structure The dashboard (see Figure 1) depicts learning content represented in learning units. In accordance with task-based language teaching (Van den Branden, 2016), each unit contains multiple learning targets for grammar or vocabulary practice (e.g., Simple Past) which the students need to acquire in order to successfully complete the final communicative target task and its functional goal. In our system, teachers can self-assemble these learning targets into learning units to align with a curriculum, thus supporting different textbooks. Additionally, teachers can define and describe the communicative goal and target task of each learning unit (e.g., Storytelling: Write a Story! Start with events in the past, describe the present, and then look into the future.), which will be presented in the dashboard header. Making this link transparent for the students in this way strengthens the connection to the functional target and purpose of practice. Within each learning target of a learning unit, a range of pedagogically motivated realizations of the learning target are listed. Yes/no questions, for instance, constitute a realization of the learning target simple past. The realizations represent the task-essential language. The system inherent domain model maintains a static, pedagogically motivated order of the learning targets, as well as of the realizations within each target, that have been manually determined by an expert teacher. This structure of the content into coarse and fine-grained content containers makes intermediate acquisition goals visible at different levels
towards the overarching functional target, which is present at all times, and thus incorporates taskorientation into a student dashboard.
The domain model in our system is based on the curriculum of seventh grade German academic track schools and currently contains 14 grammatical targets, mainly focusing on tenses (i.e., Simple Present, Simple Past, Present Perfect, Past Progressive, Will-Future, Going-to-Future), and their pairwise comparisons, as well as conditional clauses, relative clauses, and comparative forms.
Navigation Traditional systems expect high selfregulation from students by requiring them to themselves navigate through the practice material (Sun et al., 2023). Especially for weaker students, these decisions surpass their abilities so that they do better with adaptive systems (Vandewaetere and Clarebout, 2010). In order to support heterogeneous classrooms with both strong and weak students with different navigational preferences, we integrate a hybrid approach, providing options for less and more learner control over the learning content to practice. In the adaptive practice phase, neither students nor teachers choose distinct exercises, this is done by the adaptive exercise sequencing algorithm. The scope of adaptivity varies
depending on the entry point a student chooses. Highly self-regulated students may navigate more autonomously and by themselves select a realization for which the algorithm adaptively sequences exercises. Less self-regulated students, on the other hand, may let the system globally choose both the learning target realization and the exercise.
Although the order displayed via the interface reflects the static order of the pedagogically motivated domain model, the fully adaptive sequence may skip practice of certain realizations for strong students if that realization is also practiced together with other realizations in the same learning target. In the example given in Figure 1, this could for instance be the case for negative statements which are also practiced in affirmative + negative. Whether a student belongs to the group of strong students for whom realizations are skipped, is based on the student’s language proficiency level, which is determined by C-tests periodically administered via the system.
Progress and performance metrics A student dashboard serves not only to navigate to the next exercise but also to visualize the student’s progress and performance. Given the lack of sufficient training data for our target domain, we cannot use
KT model-based progress representations as commonly used in adaptive systems. In traditional systems, progress can be as simple as displaying the ratio of completed exercises out of all exercises (Duan et al., 2010). This is not suitable for macroadaptive systems, where the number of completed exercises can easily be determined, yet the number of all exercises is unknown before the student has achieved mastery. Bull and McEvoy (2003) suggest an alternative approach which displays the numbers of successfully and unsuccessfully acquired concepts.
We build our progress metric on this idea, but only display successfully acquired linguistic properties for each realization in a pie chart in order to further increase simplicity. Linguistic properties such as regular verb forms are defined at a finegrained linguistic level. Exercises are linguistically analyzed with the annotation pipeline introduced in Rudzewitz et al. (2018) using the Unstructured Information Management Architecture (UIMA, (Ferrucci and Lally, 2004)) and standard natural language processing (NLP) tools, i.e., segmentation, part-of-speech tagging and dependency parsing with ClearNLP (Choi and Palmer, 2012), lemmatization with Morpha (Minnen et al., 2001) and morphological analysis with the Sfst tool (Schmid, 2005). Based on these basic linguistic analyses of the exercise content, including the target answer and any linguistic co-text such as prompts but excluding exercise instructions, the exercise annotations are extended with more specific linguistic constructions (e.g., regular verb forms with infinitive ending in -y) they cover. This second step uses a rule-based approach with UIMA Ruta (Kluegl et al., 2016) as described by Quixal et al. (2021). The domain model hierarchically associates linguistic constructions with properties, and properties with realizations. Thus, it indirectly links annotated exercises to realizations for which they act as options for adaptive practice. Acquisition of these properties represents discrete steps towards progress completion for a realization. Progress completion is calculated based on interactions with the exercises and pre-defined accuracy thresholds per property. Students’ attempts on exercise items are analyzed with respect to correctness, therefore a student’s answer is compared to the underlying exercise’s target answer. Given that the exercise carries annotations of linguistic property, the interactions with items in the exercise result in either
positive or negative evidence for property acquisition.
Micro-adaptive adjustments while a student works on an exercise, for instance reducing the number of distractors, are not explicitly shown in the dashboard. They are implicitly incorporated in the progress metric as the adaptive algorithm takes the support a student needs into account by weighting the student’s attempts respectively.
In existing ITS, the performance achieved for a realization is often indicated based on a single exercise, be it the most recent (e.g., Harindranathan and Folkestad, 2019; Britain, 2020) or the best one per realization-inherent difficulty level (e.g., Colling et al., 2022). In our adaptive system, neither of the two makes much sense. Displaying the performance on a single exercise only makes sense if all exercises target similar properties. Since in our implementation, each realization practices various linguistic properties which are distributed over multiple exercises, a single exercise cannot be representative of a student’s current performance. Other systems use average performance over all exercises (Keleş et al., 2009). In this approach, performance visualizations of 100% can only be achieved if all answers are correct. However, students might initially provide incorrect answers based on learning gaps or misconceptions which they can overcome in the practice phase. Displaying average performance of all exercises carries the risk of demotivating or even frustrating students as they cannot receive a perfect performance once given a single incorrect answer. In our system, we want to encourage students to also attempt exercises that they cannot master at first try, to benefit from the scaffolding feedback. Pushing students to only work on exercises where they are certain to get everything correct, in order to have a perfectly polished dashboard with 100% in all performance metrics, would be counter-productive for the purpose of learning and practicing in the zone of proximal development, which describes the space of what a learner can acquire when supported (Vygotsky, 1978). Moreover, average performance is not comparable across students and learning target realizations as it does not account for the amount of practice. A metric based on three exercises would put more weight on incorrect solutions than a metric based on 50 exercises. Average values, given in percentages, in general make it less transparent and less intelligible for low literate students to connect the exercise submission
to the performance metrics. To account for compatibility, transparency and taking learners improvement over time into account, we determine performance by including the most recent ten items instead of focusing on a single submission or an average for an aggregated visualization. As exercises in our system consist of five items, this represents the performance on the last two exercises. Performance is displayed as criterion-referenced performance in a stacked bar chart, giving discrete numbers of items solved correctly at first try, correctly after feedback, and incorrect or not attempted items. This performance display proposed by Colling et al. (2022) shows independent, exam-like as well as scaffolded success and has been evaluated in terms of comprehensibility for seventh graders.
Mastery criterion In order to complete the entire learning target, students need to master all its realizations. Mastery is assessed through specific exercises, which we call diagnostic exercises. These are manually created by teachers and didacticians and tailored to align well with the practice exercises and the German seventh grade curriculum. Following Colling et al. (2022)’s approach of parallel exercises, there are multiple comparable instances of diagnostic exercises for each realization. This allows students to re-attempt the readiness check after failing a diagnostic exercise. The current diagnostic exercise for a realization is accessible via the Check readiness button and assesses the abilities needed to support the functional goal and thus the student’s readiness for the target task regarding the particular realization. It takes into account that no support is provided in the communicative task and therefore evaluates only the student’s unassisted attempts without providing scaffolding feedback. When a student achieves mastery for a realization by successfully completing its diagnostic exercise, that realization is assigned a trophy symbol. In traditional ITS, readiness to attempt the diagnostic exercise would correspond to having completed all practice exercises of the realization. As there is no predefined sequence of exercises in a macro-adaptive system, in our approach, the adaptive algorithm evaluates, while the student progresses through the adaptive sequence, if the student has practiced all linguistic properties that underlie the realization and if the student’s accuracy is at the required proficiency level. Only then can the system reliably predict that the stu-
dent will give a correct solution in the diagnostic exercise. Predicted readiness is made salient by a shiny border around the Check readiness button in addition to the full progress pie chart. If a student chooses to work on a diagnostic exercise before the system deems them ready, the system advises them to first practice some more, yet without forcing them to do so. Students are thus guided and scaffolded in the understanding of the provided analytics, in form of progress and performance metrics. This enables students to make sense of their statistical data (van Leeuwen et al., 2022) and as a result identify the next steps towards their learning goal.
Permanency of mastery Since traditional ILTS have static exercise sequences, mastery is a permanent attribute. In adaptive systems, however, forgetting needs to be incorporated (Zaidi et al., 2020). In order to consider this in the student dashboard, our trophies "gather dust" once the adaptivity algorithm ascertains that mastery has expired, as demonstrated in Figure 2. This happens if a student hasn’t actively – as part of a gap in a gapfilling exercise – or passively – as part of the gaps’ co-text – practiced the realization for a set time, which is adjusted based on a student’s retention capacities tracked in the learner model. By revising a realization through clicking on the Check readiness button – after optionally completing additional practice exercises –, students can prove their maintained proficiency to the system and to themselves. The trophy then regains its shiny appearance.
Homework assignment Teachers who use an ITS as assistant tool for classroom teaching often desire to assign specific exercises to their students which they can then discuss with the entire class (Singh et al., 2011). This is no problem in traditional systems where all exercises are listed and can directly be accessed by the students. In macroadaptive systems, however, the algorithm dynamically determines the concrete exercise instance
which a student practices. Additionally, in microadaptive systems each student receives individually tailored exercises. Consequently, no list of all exercises that is shared amongst all students is available. In order to still facilitate the presented scenario, our system allows teachers to specify mandatory exercises which all students have to complete. By thus giving teachers certain control over the learning content, they can ensure that all students have been exposed to a specific set of hand-selected exercises. The adaptivity algorithm automatically integrates these mandatory exercises into the exercise sequence at the appropriate position according to their associated learning target realizations and the individual student’s learning state. However, some students might wish to specifically practice these exercises, either because their slow progress prevents them from reaching the mandatory exercises within the adaptive sequence in a feasible amount of time, or because they do not see the need for additional practice as they might already be proficient in the respective realization. We therefore also explicitly list the mandatory exercises with the option to open them directly. Similarly to the behavior when attempting a diagnostic exercise, if a student chooses to practice a mandatory exercise for which they are not yet proficient enough according to the adaptivity algorithm, the system recommends to first practice more. Students can always decide to ignore these recommendations and proceed to the selected mandatory exercise.
These considerations allow the system to provide common student dashboard features of task-based ITS while also integrating user-adaptivity.

heading: 4 Discussion
text: 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.
Diagnostic exercises In our approach, students can attempt diagnostic exercises by clicking the Check readiness button either next to a learning target realization or globally for the entire learning target comprising all its realizations. On the one hand, this global entry point is in line with the adaptive approach requiring low self-regulation. On the
other hand, students using the global button receive all diagnostic exercises in succession so that they do not directly follow the exercises which prepare for them. This decouples the diagnostic exercises for a realization from their scaffolding practice exercises. If the global Check readiness button was removed, the question would remain whether all students should proactively attempt the diagnostic exercises themselves – which would potentially result in the same dilemma for students following the adaptive sequence, as they would not be assisted in when to attempt which diagnostic exercise. A solution could be to integrate the diagnostic exercises into the adaptive exercise sequence and saliently flag them for students. Students should then get the choice to attempt the exercise or practice more.
Transparency The subject of mandatory exercises leaves an additional question to be addressed. While displaying them globally for the entire learning target avoids the issue of being inaccessible ondemand, it also removes visual assignment to any realization. Since the aim of explicitly listing the realizations is to also foster meta-linguistic knowledge (Godwin-Jones, 2021), neglecting this aspect for mandatory exercises is questionable. Moreover, this would make it harder for students to autonomously reconstruct progress and performance updates from exercise submissions, thus resulting in higher mental load. The lack of transparency in linking exercises to realizations is also an issue for exercises accessed via the global adaptivity buttons More practice and Check readiness for the entire learning target. This could potentially be addressed by highlighting the associated realization upon opening the exercise.
Performance visualization A further discussion point concerns the visualization of student performance. While we have presented an approach to display it as criterion-referenced performance on the most recent items, multiple alternative aggregations and visualizations are envisionable. Representing mastery estimates of concepts (Tong et al., 2022), taking the average performance over multiple exercises, adding up the scores for a defined number of items or only displaying those of the most recent exercise are all valid options (Van Labeke et al., 2007; Harindranathan and Folkestad, 2019). Instead of aggregating multiple exercises, the student dashboard could also visualize all completed exercises for a student individ-
ually. This would also make it possible to mark mandatory exercises. However, if a student practices a lot, the dashboard could quickly become crowded and therefore poorly accessible on small displays (Bull and Kay, 2016). Reducing the exercises to dot representations might increase manageability. Criterion-referenced performance for each exercise could then be displayed on demand after clicking on a dot. In order to increase the discriminability of dot representations, different colors could indicate certain properties of the exercises such as the exercise type, the exercise dimension, or the proficiency level. Making the exercise type salient could for example address varying complexities as for example gap-filling exercises are considered more complex than multiple-choice exercises (Medawela et al., 2018), or varying foci on language dimensions (Grellet, 1981, p. 5) inherent to different exercise types. It would, however, still fail to consider differences in exercise complexity within each exercise type, for instance based on the number of distractors in multiple-choice exercises (Heck et al., 2022). Using the exercise dimensions of receptive, interactive and productive types (Vetter, 2012) instead would reduce the number of categories and thus increase heterogeneity within each category. Since macro-adaptivity aims to gradually increase exercise complexity, the different categories would for both options inadvertently display scores at different stages of the learner’s progress, which might not be transparent to students. The alternative approach to associate exercises with the learner’s proficiency level at the time of completing them translates continuous proficiency scores of a KT model into concrete categories. Considering the small number of categories, this is also feasible with KT models of moderate accuracy. However, since a student’s progress is not always linear (Shirai, 1990) nor are there clear thresholds between the levels, this approach might not give helpful insights either. A compromise between representing all exercises and using a single global aggregation could alternatively collapse exercises with similar colors into a single dot representation with the number of collapsed exercises indicated inside the dot. This would, however, lose the benefits of the non-collapsed representation of highlighting mandatory exercises and providing anchors for criterion-referenced performances per exercise.
Progress visualization Although we choose to base our progress measure on linguistic properties, this does not necessarily have to be the case. The categories of exercise type, exercise dimension, and exercise complexity suggested for a performance metric can also be considered for progress. However, categorical progress units, which increase in discrete steps, incorporate ranges of continuous values so that progress does not necessarily increase after each exercise. While KT in principle facilitates continuous and constantly perceivable progress updates, we have already argued that the model’s estimates are not accurate enough with insufficient training data.
Customized learning units Finally, an adaptive system that supports multiple curricula allows teachers to compile their own learning units. Ideally, teachers can also exclude certain linguistic properties which they do not (yet) wish to practice. Since they may later decide to include these properties, students who have already received a trophy might not fulfill the requirements anymore when also considering the newly included properties. Withdrawing the trophy could be discouraging and the underlying reasoning might not be intuitive to students. A possible solution could use the mechanism of gathering dust so that the trophy would still be visible but inactive. Additionally, the progress pie chart for the realization would have to change accordingly. Making these changes transparent and intelligible for students is not trivial, especially considering that young learners’ graphical literacy skills may still be developing (Roberts and Brugar, 2017). It becomes even more of a challenge if multiple learning units practice the same learning target, thus sharing the same pool of exercise candidates. This is especially relevant for teachers who want to incorporate a revision learning target, e.g., having one learning unit where students first learn simple past, maybe not including all linguistic properties, but also including simple past as a revision when introducing conditionals type 2 in another learning unit. The question then arises whether performance should be calculated separately within each learning unit – which would hinder the adaptivity algorithm as it would not be able to globally track the students’ learning progress – or synchronize progress across the units. Synchronizing progress for realizations where different linguistic properties have been excluded is, however, unfeasible. On the other hand,
disallowing teachers to exclude different properties for different units might also not result in the desired functionalities, especially if teachers intend to practice complementary properties of a realization in different learning units. From a student’s perspective, working on a learning target realization in one learning unit but receiving a performance update in another unit as well might lead to misunderstandings, demotivation, or distrust in the technology due to the poor user experience (Franconeri et al., 2021). In the worst case, it could even result in negative learning outcomes. From a teacher’s perspective, interpreting and assessing duplicates of identical performance history items in multiple units might be challenging and tedious. Especially the display of mandatory exercises in synchronized learning targets constitutes an open issue.

heading: 5 Conclusion
text: 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 REACT1 and build into the modular architecture of the ILTS FeedBook (Parrisius et al., 2022), 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
1https://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.

heading: Acknowledgements
text: We want to acknowledge the teachers in the AI2Teach project, especially Florian Nuxoll, for their input and feedback.

heading: Future Work