Towards Task-Oriented ICALL: A Criterion-Referenced Learner

Dashboard Organising Digital Practice

Leona Colling

1,6

, Ines Pieronczyk

2,6

, Cora Parrisius

2,3,6

, Heiko Holz

1,4,6

, Stephen Bodnar

1,6,7

Florian Nuxoll

1,6

and Detmar Meurers

1,5,6

Department of Linguistics, University of Tübingen, Germany

Hector Research Institute of Educational Sciences and Psychology, University of Tübingen, Germany

Karlsruhe University of Education, Germany

Ludwigsburg University of Education, Germany

Leibniz-Institut für Wissensmedien (IWM), Germany

LEAD Graduate School & Research Network, Germany

Tübingen Center for Digital Education, Germany

Keywords: Second Language Acquisition, Task-Supported Language Teaching, Intelligent Computer Assisted Language

Learning, Learner Dashboard, Criterion-Referenced Feedback.

Abstract: Practice is an essential part of learning. Intelligent Computer-Assisted Language Learning (ICALL) systems

can provide practice opportunities and give insights into the learner’s learning state and progress. Open learner

models have been designed to provide learners with information on the overall learning domain. However,

current approaches to foreign language teaching typically motivate practice as preparation for a communica-

tive or functional task. This raises the question of how this motivating functional task context and progress

towards mastering the task-essential language can be made explicit in an ICALL system. We present an ap-

proach to ICALL practice that is orchestrated in a dashboard that provides information on the learner’s com-

petence-oriented learning progress towards the overall task goals. The dashboard allows students to choose

what to practice next based on this information, which provides a transparent, motivating link to the purpose

of practicing. Organising digital practice based on a task- and competence-oriented curriculum also facilitates

the acceptance of ICALL in the formal school setting. The dashboard introduced in this article extends the

intelligent tutoring system FeedBook for English in German secondary schools. The article provides the the-

oretical background for the dashboard’s structure, motivates the design process, and describes the resulting

implementation.

1 INTRODUCTION

While practice has long been an integral part of for-

eign language learning, Skill Acquisition Theory

(DeKeyser, 2020) has scientifically motivated the

role and importance of practice in Second Language

Acquisition (SLA) research. Systematic practice ena-

bles learners to proceduralise and partially automatise

language production (DeKeyser, 2010). In this con-

text, new technologies such as Intelligent Computer-

Assisted Language Learning (ICALL) systems offer

a great opportunity to enhance the learning experi-

ence and examine practice behaviour in authentic

school contexts (Meurers et al., 2019; Ruiz et al.,

2023). ICALL systems can provide exercises for

practice with adaptive feedback, track the learner in-

teraction with the system, and display insights on the

language learning progress (Rudzewitz, 2021), paral-

lel to what has been established for other learning do-

mains, such as arithmetic (Molenaar & Knoop-Van

Campen, 2016). Based on interaction data, the sys-

tems can build an internal representation of the

learner’s knowledge and misconceptions across the

subject domain, the learner model. When it is also

made accessible to the user, it is often referred to as

an Open Learner Model (OLM, Bull, 2004). Bodily

et al. (2018) emphasise the beneficial effects of the

use of data gathered and visualized in an ICALL sys-

tem on the learner’s reflection and self-regulation, re-

lating it to the field of data-driven learning analytics

as well as a pedagogical point of view.

At the same time, the implementation of ICALL

in authentic school contexts is very slow (Schmidt &

668

Colling, L., Pieronczyk, I., Parrisius, C., Holz, H., Bodnar, S., Nuxoll, F. and Meurers, D.

Towards Task-Oriented ICALL: A Criterion-Referenced Learner Dashboard Organising Digital Practice.

DOI: 10.5220/0012753000003693

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024) - Volume 1, pages 668-679

ISBN: 978-989-758-697-2; ISSN: 2184-5026

Strasser, 2022). One aspect holding back its imple-

mentation is that exercises generally need to be em-

bedded into a pedagogical sequence of introduction,

systematisation, and practice of the language means

toward functional language use (Brauner & Prediger,

2018). Indeed, successful participation in an authen-

tic, often communicative task at the end of a learning

unit forms the central goal of task-oriented teaching

approaches, such as task-supported language teaching

(TSLT; Müller-Hartmann & Schocker von Ditfurth,

2011) and task-based language teaching (TBLT; El-

lis, 2003; Robinson, 2011; Willis & Willis, 2007). It

has become standard in German school curricula for

foreign language learning to centre language instruc-

tions around such tasks.

Pre-task activities offering practice opportunities

can build up towards the task goal (Vogt & Schmidt,

2021). These systematic activities allow learners to

practice task-essential language aspects as part of

homework or during individual learning times. They

provide a natural way of integrating ICALL practice

into current task-oriented foreign language class-

rooms. Technology-mediated TBLT, where technol-

ogy is used to aid TBLT, has been qualitatively ex-

amined (see Chong & Reinders, 2020) and research-

ers even identified design patterns for task-based

technology-enhanced language learning (Canals &

Mor, 2020). However, in contrast to ‘strong’ TBLT,

where focus on forms is incidental, the school reality

in Germany is more in line with TSLT, where linguis-

tic forms are taught explicitly prior to carrying out a

meaningful, contextualised, and interactive task

(Kolb & Raith, 2018; Kos 2023). To the best of our

knowledge, we here present the first ICALL approach

for large-scale use in K-12 classrooms explicitly de-

signed to facilitate the TSLT pre-task practice phase.

This paper discusses how an ICALL system can

be extended with a learner dashboard to motivate and

embed the practice activities as part of a task-sup-

ported curriculum. First, we will introduce OLMs and

TSLT as the foundations for our learner dashboard

design. Second, we will describe our concrete realisa-

tion of a task-based learner dashboard in an ICALL

system for English learners in secondary schools.

2 COMBINING OPEN LEARNER

MODELS & TASK-SUPPORTED

LANGUAGE TEACHING

Computer-assisted language learning (CALL) has a

long history dating back to the 1960s (Davies et al.,

2013). While interest in CALL rose in the 1990s

(Chen et al., 2020), Intelligent CALL (ICALL) is a

more recent research strand employing methods from

natural language processing and artificial intelligence

(Schulze & Heift, 2013). Complementing these meth-

ods to analyse language and provide feedback,

ICALL approaches realising Intelligent Language

Tutoring Systems (ILTS) also maintain learner mod-

els to monitor progress and adapt system interaction

(Heift & Schulze, 2003). Sometimes this information

is also made openly available to the learner (see e.g.,

Bull & Pain, 1995). After providing some back-

ground, we raise the question of how we can organise

such information about the learner and the learning

process to facilitate interaction in relation to an over-

arching didactic approach, such as task-supported for-

eign language teaching as a prominent paradigm.

2.1 Open Learner Models (OLMs)

Bull and Wasson (2016) characterize OLMs as visu-

alizations that are accessible for the learner and are

“based on an underlying inferred model of the

learner’s current competences or understanding, ra-

ther than behaviour or performance data logged” (p.

151). This is rooted in a long tradition of learner mod-

els as a representation of the learners’ knowledge of

a subject domain as a whole (Bull & Kay, 2010). Such

learner models are a core component of adaptive dig-

ital learning environments (Brusilovsky & Millán,

2007) to let the system act on the basis of the inferred

student learning status, competencies, or misconcep-

tions (Bull, 2004). Making use of the same type of

information, OLMs provide the learners with infor-

mation about their learning to help them monitor and

regulate it (Bodily et al., 2018; Bull & Kay, 2010).

In the SLA context, OLMs have been imple-

mented in some systems, primarily in the higher edu-

cation context (e.g., Bull et al., 2016; Bull & Wasson,

2016; Tsourounis & Demmans Epp, 2016; Xu & Bull,

2010). To depict the current inferred learning state to

the learner, skill meters, gauges, stars, or radar plots

have become established visualisations. Learners in-

dependently explore these visualisations of their per-

sonal learner model representing their knowledge of

the overall curriculum. This is complex and may be

the reason why there is substantially less work on

OLMs with younger learners – though Y. Long &

Aleven (2017) is of relevance here, and in Rudzewitz

et al. (2020) we presented an OLM for the secondary

school English context.

While OLMs provide a wealth of information on

competency states and misconceptions on each of the

many different subdimensions of the subject domain,

Towards Task-Oriented ICALL: A Criterion-Referenced Learner Dashboard Organising Digital Practice

669

it is often not transparent for the learner how complet-

ing an exercise contributes to the system’s record of

the overall learning state. The complexity of the cur-

riculum and how different types of exercises instanti-

ate the relevant language competencies contrasts with

the learners’ need for easily accessible information.

Hard to understand visualisations will fail to impact

learning behaviour (Bull, 2004; Ring et al., 2019).

Going beyond the need to present the relevant infor-

mation transparently, OLMs exclusively reporting the

students’ learning status for the subject domain are

missing an organisational and actionable link to the

learning goal and context. They lack a connection to

an overall pedagogical plan that outlines what needs

to be learned and motivates its relevance.

2.2 Task-Supported Language

Teaching (TSLT)

The emphasis on functional learning goals is part of

the foreign language didactic approach of task orien-

tation, TSLT or TBLT (Ellis, 2003). While TBLT fo-

cusses on authentic language use from the beginning

and incorporate explicit grammatical practice only in-

cidentally, TSLT includes explicit grammar practice

prior to meaning-oriented language use and is closer

to school reality in Germany. Task-orientation is

firmly established in the German curricula for English

as a foreign language (Müller-Hartmann & Schocker

von Ditfurth, 2011).

Communicative tasks, usually performed in class,

form the centre of learning, instruction, and assess-

ment (Spada, 2021). Ellis (2003) compiled a list of

definitions for “task” in the foreign language context,

which stress the task as an activity designed to func-

tionally reach an objective. Since functionally suc-

cessful language use is generally seen as the ultimate

goal of foreign language learning, tasks sometimes

are also referred to as “target tasks” that students

should be able to perform (Long, 1985).

To be able to solve the target tasks, a “pre-task”

phase can help students practice the task-essential

language, i.e., the relevant language means (e.g., con-

ditional clauses), while remaining meaning-focused

(Ellis, 2003). In TSLT, this phase prior to the target

tasks includes explicit form-focused exercises.

2.3 Learner Models and Task

Orientation in Learner Dashboard

Design

Building on the increasing attention to the interplay

of technology, learners, and pedagogy (Lai & Li,

2011), it would be beneficial to incorporate more

learner-centred design for learning principles into

CALL research and practice, also adapting to the de-

mands of classroom settings that are ecologically

valid (Sun, 2017). OLMs provide such learner-ori-

ented means of supporting learners to actively shape

their own learning and pursue functional goals. Yet,

OLMs aimed at infering the students’ knowledge

about the subject domain from the interaction data

generally do not split the gathered information into

subsets that are relevant for particular communicative

functional language use.

For example, a student answer to an exercise that

is part of the pre-task phase of a certain task may con-

tain errors regarding multiple concepts (e.g., irregu-

lar past tense verb form and conditional clauses type

2). Some of these may be the pedagogical focus in a

different target task, but information about the student

performance on these concepts is updated in the

learner model independently of the target task. The

performance scores of concepts that are not the cur-

rent focus of instruction thus may change in ways not

transparent to the learner (Mabbott & Bull, 2004).

While OLMs provide relevant information for the

learner, they were not designed to relate the learner’s

state and development of knowledge to a pedagogical

learning goal. So, they do not indicate progress to-

wards target tasks as part of a foreign language school

curriculum as envisaged under a TSLT approach. Es-

tablishing this link also facilitates the effective imple-

mentation of ICALL systems in real-life school con-

texts since it then transparently supports TSLT as the

established didactic approach. We therefore propose

to combine OLM with the TSLT perspective by de-

veloping a transparently structured learner dashboard

that provide insights on a student’s learning status and

progress towards goals related to target tasks.

To design a learner dashboard that combines

OLM functionality with the TSLT perspective, we

need to spell out the components of such a student,

learner, or learning dashboard. For Schwendimann et

al. (2017), “a learning dashboard is a single display

that aggregates different indicators about learner(s),

learning process(es), and/or learning context(s) into

one or multiple visualisations” (p. 37). The relevant

information can be derived from click-stream behav-

ioural data, meta-information of the learning material,

learner models, or a combination of these sources.

According to Verbert et al. (2013), student dash-

boards should (1) make learners aware of their current

learning status, (2) raise questions concerning the rel-

evance of the displayed information for one’s own

learning, (3) provoke new insights, and (4) impact the

learners’ following learning behaviour. These stages

take effect if the single display of a learner dashboard

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670

includes an explicit learning goal and valid, aggre-

gated information on students’ progress towards this

learning goal or target task. We will argue that this

can be achieved if the learner dashboard contains cri-

terion-referenced feedback (Brown & Hudson, 2002).

What is criterion-referenced feedback? A target

task or learning goal requires certain linguistic or

communicative competencies, basically pre-estab-

lished criteria, to be fulfilled by the learners

(Mirmakhmudova, 2021). Although the term “crite-

rion-referenced” appears more frequently with “as-

sessment” or “testing,” where performance is defined

by achieving pre-defined criteria and not in relation

to other students’ performances (Lynch & Davidson,

1994), we here emphasise the value of this criterion-

referenced measurement in a practice context. Setting

acquisition criteria is a common approach in SLA:

Learning a language is often regarded as a gradual de-

velopment, which means that certain linguistic struc-

tures need to be emerged before others can be ac-

quired (Pallotti, 2007). Criterion-referenced feedback

considers learners’ individual performance to these

criteria (González-Marcos et al., 2019). For example,

to write a report, learners need to be able to use the

simple past and build regular and irregular verb

forms. This is in accordance with TSLT’s emphasis

on the complex target tasks’ communicative de-

mands, which form the centre of this task-oriented

teaching and learning (Robinson, 2011). Criterion-

referenced feedback enables learners to obtain relia-

ble information on their progress towards pre-defined

goals and, thus, their ability to solve the target tasks

(Mirmakhmudova, 2021). The success of including

criterion-referenced feedback in students’ learning

has already been shown in analogue learning environ-

ments: Criterion-referenced feedback was revealed to

be more effective on students’ task performance than

individual- or social-referenced feedback (Wilbert et

al., 2010; Wollenschläger et al., 2011). However, re-

search on criterion-referenced feedback and learner

dashboards has so far been mainly conducted in

higher education contexts (Schwendimann et al.,

2017).

In the following, we will present a concrete ap-

proach to include criterion-referenced feedback in the

context of task-supported learning in secondary

school classrooms. For this, we enriched a practice-

oriented ICALL system for English as a foreign lan-

guage with a learner dashboard depicting students’

progress towards pre-defined criteria and target tasks.

3 DEVELOPMENT OF A

CRITERION-REFERENCED

LEARNER DASHBOARD

We, a team of system developers, educational re-

searchers, and SLA experts supported by English

teachers, developed a so-called criterion-referenced

learner dashboard that serves multiple functionalities

to support students’ language learning and metacog-

nitive learning to learn (European Schoolnet, 2014;

Fredriksson & Hoskins, 2008). On the one hand, the

dashboard serves as a performance and progress view

that gives students insights into their current learning

status with respect to the criteria for the target task.

On the other hand, it offers the navigation and selec-

tion of exercises, including additional practice oppor-

tunities to further progress towards the mastery of the

language means needed for the target task. The dash-

board’s clear connection to the communicative target

task is realised by listing the required grammatical

and lexical criteria to fulfil the functional communi-

cative goal stated in the header. This strengthens the

link between the function and the form-focused prac-

tice.

Tailored to support the German school reality, the

system is designed for TSLT classrooms, and best in-

tegrated into the pre-task practice phase, where lan-

guage means are taught explicitly, while maintaining

the functional goal in mind. In TBLT classrooms, our

system best fits into the post-task phase where re-

maining knowledge gaps are being explicitly ad-

dressed with additional practice.

The dashboard is embedded in the existing ILTS

FeedBook (Meurers et al., 2019), which provides im-

mediate, scaffolded feedback on learners’ responses.

Scaffolded feedback provides incremental hints to en-

able the students to get from an initially incorrect an-

swer to the correct one (Finn & Metcalfe, 2010). Each

exercise in the FeedBook consists of multiple items

to be solved, for instance, each gap in a gap-filling

exercise represents one item. The FeedBook provides

scaffolded feedback on the students’ response per

item (Rudzewitz et al., 2018; Ziai et al., 2019). It has

been shown to positively impact students’ language

proficiency (Meurers et al., 2019).

3.1 Task-Related Structure of

Learning Content

In TSLT, the goal is to be successful in the functional

target task. To prepare for a target task, students prac-

tice the linguistic components needed with a given set

of exercises that, together with the target task, form a

Towards Task-Oriented ICALL: A Criterion-Referenced Learner Dashboard Organising Digital Practice

671

task cycle

. Our goal was to integrate this approach

into the digital tool. Therefore, didactic experts de-

signed a variety of exercises for the FeedBook to ex-

plicitly foster students’ acquisition of linguistic com-

petencies required for the target task (Schmidt &

Strasser, 2022). The digital exercises are thus in-

tended to augment, not replace the classroom-based

instruction. The content of the task cycles and thus the

practice in the FeedBook is aligned with the German

seventh-grade curriculum for English as a foreign lan-

guage in academic-track schools.

For the development of our dashboard, we com-

pleted a few structural steps concerning the learning

material. Following the TSLT approach, we defined

criteria (i.e., grammatical and lexical language means

that students must master to solve or successfully par-

ticipate in the cycles’ specific target task), which cor-

respond to entities in the systems inherent learner

model, and structured the exercises accordingly. Each

language mean can be practiced in several exercises.

To associate an exercise with a language mean, the

main pedagogical objective of the exercise has been

annotated manually by the didactic experts. In conclu-

sion, the visual layout of the exercise structure is

aligned with the internal learner model and thus the dis-

connect between practice and learner model decreases.

Based on this idea of embedded practice in task cy-

cles, we developed the dashboard so that each cycle has

its own corresponding dashboard view. The dashboard

header (see Figure 1) provides the task orientation de-

scription by stating the target task title and the commu-

nicative goal. This header ensures that the task and,

thus, the functional connection of the exercises, is al-

ways present and visible to the user. The dashboard can

thus help to remind the students what they practice for

during individual working times, such as homework –

when no one else is there to motivate them.

Figure 1: Task-oriented header for cycle 1, including the

goal of the communicative target task, the different topics

(called “sections”).

Since each target task requires mastery of certain lan-

guage means, these means are listed in the dashboard,

and their associated exercises can be accessed via a

An example of a target task for which the system pro-

vides digital practice exercises can be found in Pili-

Moss et al. (2024).

click on the button labelled “practice” (see Figure 3).

Students can work on these exercises in any order.

Fine-grained language means (e.g., “gerunds after

prepositions,” “gerunds as subjects,” or “gerunds as

objects”) are clustered into more coarse-grained

grammatical topics (e.g., “grammar – gerund”) and

lexical topics (e.g., “words and phrases”). All topics

of a cycle are accessible via the header (see Figure 1

and 3 as an example for Cycle 1). Only the language

means of one topic are displayed at a time. This user-

friendly structuring helps to reduce overloading the

screen and therefore reduces scrolling down a list of

20 language means on one page, which can become

tedious on small screen devices such as smartphones

(Trewin, 2006).

3.2 Criterion-Referenced Performance

Feedback

Besides the task-oriented header and the listing of rel-

evant language means, each topic’s dashboard view

contains multiple components that together compose

the learner dashboard.

The criterion-referenced performance feedback

bar provides criterion-referenced information on stu-

dents’ performance and operates on the exercise level

(see Figure 2 and 3). Performance bars are a typical

game design element having the potential to satisfy

learners’ need for competence and to communicate

the meaningfulness of the exercise (Sailer et al.,

2017). Performance feedback on the exercise level is

represented in an easily understandable horizontal

stacked bar chart consisting of three parts (see Figure

2), each stating the absolute number of exercise items

that fall into the category:

• Correct at first try (dark turquoise): If the

student’s initial response for an item

matches the target answer (or one of multi-

ple target answers).

• Correct after feedback (light turquoise): If

the initial response for an item was incorrect

but the student managed to get to the correct

answer with the help of the scaffolded feed-

back provided via the system, regardless of

the number of attempts.

• Incorrect or missing (grey): If the student

submits an incorrect response or leaves an

item unanswered.

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672

The number of hints received per individual item is

not considered for the three categories, as taking feed-

back hints should not be seen as a punishment. The

performance category description is available via the

legend panel in the header (see Figure 3). Such per-

formance bars provide a summary of the student’s in-

teraction with the exercise and count towards the ac-

quisition of the respective language mean – and the

latter is the ultimate criterion to be mastered for a suc-

cessful participation in the target task. This holistic

performance measurement on the exercise level is the

foundation for the aggregated view in the criterion-

referenced dashboard.

Figure 2: Exercise level performance stacked bar chart,

consisting of three parts. One bar is mapped to one exercise;

the different performance categories are colour coded. Dark

turquoise for “correct at first try,” light turquoise for “cor-

rect after feedback,” and light grey for “incorrect or miss-

ing.” The numbers in the circles represent the absolute num-

ber of items from the exercise falling into the respective

performance category.

This aggregated dashboard view in Figure 3 visual-

ises the learners’ criterion-referenced performance on

the language mean level, namely the completion ac-

curacy. Per language mean, the exercises are ordered

in the dashboard by increasing difficulty, from the

least to the most complex. The difficulty level inher-

ent to the exercise has been defined on a global level

for all students by the didactic creators of the activity.

Within this aggregated view, the individual perfor-

mance bars are all scaled to the same size to be easily

comparable.

Figure 3: Criterion-referenced dashboard view for Cycle 1

– grammar topic “simple past,” including the following

components: (1) header, (2) expanded legend, (3) language

means, (4) criterion-referenced performances, and (5)

“more practice” functionality.

3.2.1 Design Procedure of the

Criterion-Referenced Performance

Bar

The development and design of the criterion-refer-

enced visualisation and the final dashboard was an it-

erative process.

The idea of the stacked bar charts was based on

existing dashboards, OLM visualisations, as well as

learning games. However, none of the existing visu-

alisations fit our purpose entirely, namely being suit-

able for seventh graders, complying with the exercise

types and feedback format in the FeedBook, and ena-

bling task orientation. The dashboard design process

contained the involvement of the target group and the

implementation of current scientific design principles

(e.g., Bennett & Folley, 2019; Bodily et al., 2018;

Bodily & Verbert, 2017; Sedrakyan et al., 2020).

To ensure comprehensibility for the users, we

conducted two empirical studies. First, we ran an ex-

plorative user study. In this study, we interviewed six

seventh-grade students using a “think-aloud” method

and a guided interview to obtain qualitative insights

into their understanding of the planned components.

During the think-aloud part, students watched a

screencast showing a fictional student using the Feed-

Book and the current dashboard. They needed to de-

scribe what was happening and interpret what they

could see. During the subsequent interview, we

showed them certain features again and asked them

explicitly to explain their meaning. We presented the

stacked bar chart accompanied by stars. These stars

tried to map the criterion-referenced performance,

split in its three categories, onto one combined score.

We also presented them, as a variation to the stacked

bar chart, two distinct metrics. One represented the

count of correct responses at submission time as ab-

solute numbers and one represented the percentage of

successfully used hints (i.e., hints that resulted in up-

take, and thus, correct solution). These separate met-

rics were less understandable and thus, we opted for

the combined criterion-referenced performance bar.

Second, 36 sixth-, seventh-, and eighth-grade students

participated in an online survey with which we eval-

uated their understanding, preference, and the com-

ponents’ adequacy. According to the results, the cri-

terion-referenced bar chart as a performance metric

was easy to understand for the target group. We did

not add stars to the bar chart for the final implemen-

tation, because we could not see a surplus value. The

mapping was not trivial in terms of weighting the dif-

ferent criterion-referenced categories regarding the

combined score, which was also reflected in the stu-

dents’ ratings, where they were required to assign a

Towards Task-Oriented ICALL: A Criterion-Referenced Learner Dashboard Organising Digital Practice

673

star score to a particular bar chart. The star scores

(one to three stars, allowing for half stars) that the stu-

dents set differed hugely between students and from

the automatically calculated system score. Moreover,

the system lacks a functionality that enables students

to utilise collected stars as a form of currency for

making purchases, which consequently may cause

frustration. By implementing these decisions, the

dashboard’s complexity is reduced as it only needs to

display a single combined metric instead of two.

For the bar chart itself, we decided against signal

colours such as red to avoid evoking strong negative

emotions. In addition, to accommodate for colour

blindness, we decided against a distinct colour per cri-

terion-referenced category, and instead opted for the

bar chart ranging from a dark turquoise to a light grey,

with the most intensive, brightest colour representing

the target category “correct at first try.” The bar

makes the distinction between “correct at first try”

and “correct after feedback” salient to the learner. It

could technically be generalised over all exercise

types the system provides (e.g., gap filling, multiple

choice, jumbled sentence) and is thus a consistent

measurement.

3.3 “More Practice” Opportunities –

Distinction in Core and Parallel

Exercises

In addition to the difficulty level, each exercise is

manually annotated as either core exercise or parallel

exercise. Core exercises are mandatory exercises.

Each parallel exercise, in the interface labelled as

more practice, is similar to its corresponding core ex-

ercise in terms of targeted competence, exercise type,

and exercise difficulty but may differ in terms of vo-

cabulary and syntax. Parallel exercises were designed

to create enough practice opportunities to be able to

reach the competence level needed to fulfil the goal

and master the target task. These exercises thus ena-

ble students to improve their performance for a lan-

guage mean at a particular difficulty level. Students

can develop from system supported, scaffolded suc-

cess (i.e., “correct after feedback”) to independent

success (i.e., “correct at first try”). Core exercises are

directly accessible via the dashboard, and parallel ex-

ercises are accessible via the “more practice” button

after their respective core exercise has been com-

pleted. Usually, the system offers multiple parallel

exercises per core exercise (i.e., there are several core

exercises per language mean, and one core exercise

per difficulty level). The aggregated dashboard view

(see Figure 3) displays one criterion-referenced per-

formance per difficulty level per language mean. If a

student has worked on multiple parallel exercises,

only the best submission for this difficulty level is dis-

played. Depicting only their best submissions per dif-

ficulty level in the standard view might help students

to focus on their progress and successes. By being

able to hide less successful attempts, they might be

more confident to show their dashboard view to oth-

ers, including their parents or teachers. Moreover,

learners can unfold the aggregated view by clicking

the yellow exercise button to display all submissions

for the language mean difficulty level (see Figure 4).

Through this unfolding of the submission history,

learners can see the submission of the core and paral-

lel exercises ordered by submission date. The submis-

sion history can therefore be seen as an option to gain

insight into the temporal progress with respect to the

specific criterion (i.e., the language mean).

If students click on an exercise button in this his-

tory, they can decide to either look at their exercise

submission again or re-open the exercise in practice

mode (without their previous answers).

Figure 4: Language means exercise difficulty progression

history unfolded for exercise 1A. The trophy on the top left

indicates mastery of the language mean “regular verbs.”

3.4 Language Mean Mastery –

Proceeding to the Target Task

We have taken an additional step by incorporating a

mastery measurement called “ready-to-go-ness.” It is

visualised as a small trophy icon and shows the stu-

dents whether their performance on a language mean

reached a certain level indicating if they are ready to

proceed to the target task (see Figure 4). The idea to

use a trophy, a badge, is derived from the gamifica-

tion literature: Badges, similar to performance bars,

have the potential to foster motivation and engage-

ment by emphasising learners’ competence (Hamari,

2017; Sailer et al., 2017). Gamified elements have

also been the subject of research in CALL systems

and have been reported to positively impact learners’

learning experiences and outcomes (Dehghanzadeh et

al., 2021). Goal setting can increase students’ engage-

ment and motivation (He & Loewen, 2022), which in

turn can enhance the pedagogical effectiveness of

digital learning environments (Bodnar et al., 2016).

EKM 2024 - 7th Special Session on Educational Knowledge Management

674

The learner's intention to earn a badge (i.e., mastery

trophy) can be regarded as such a goal. To prevent

negative effects of badges, they are only visible to the

students themselves, not peers (see Kyewski &

Krämer, 2018).

What is required of students to earn a trophy? Ac-

curacy of a language mean alone might not be a suf-

ficient indicator of mastery (Pallotti, 2007). There-

fore, we decided that ready-to-go-ness for a language

mean comprises two components: accuracy and ef-

fort. To address the effort component of ready-to-go-

ness, students must work on and submit all exercise

difficulty levels in the language means cluster. Thus,

exercises which have only been opened and submitted

without student answers do not count towards meet-

ing the effort requirement.

To address accuracy, students must reach a per-

formance threshold at the diagnostic exercise of the

language mean. The diagnostic exercise is the exer-

cise with the difficulty level that is required to solve

the respective target task. In our dashboard, it is al-

ways the bottom exercise of each language mean. Stu-

dents earn a trophy, and thus, are “ready to go,” if they

solve at least 60% of the items “correct at first try” on

the diagnostic core exercise or one of its parallel ex-

ercises. We defined mastery exclusively as “correct at

first try” because this resembles an exam or test situ-

ation. Our decision to settle on this fixed threshold

has multiple reasons. For this first version of ready-

to-go-ness, we focused on introducing a measurement

that is consistent throughout the whole system, works

on all exercise types, and, thus, is transparent to the

students and teachers. In a later stage, one could im-

agine setting a data-driven threshold per exercise or

empirically refining the hardcoded threshold. How-

ever, submission data on the exercises is a prerequi-

site for both solutions. The definition of mastery can

also be accessed by the users via the legend in the

header. The earlier mentioned parallel exercises ena-

ble learners to improve and reach the required thresh-

olds for a ready-to-go-ness trophy. The mastery crite-

rion ready-to-go-ness is applied to each language

mean.

Additionally, if students have achieved 100%

“correct at first try” on a difficulty level (e.g., exercise

1C in Figure 4, the student has seven out of seven

items “correct at first try”), the phrasing on the button

changes from “more practice” to “challenge me.” The

different wording indicates to high-performing stu-

dents that they have achieved an adequate level of

proficiency in this language mean. However, it also

provides additional opportunities for practice to fur-

ther improve or showcase their skills.

The criterion-referenced performance bars, the

mastery criterion visualised as trophy, and the “chal-

lenge me” phrasing can help students identify their

strengths and weaknesses in relation to the target task.

Thus, the dashboard presumably fosters students’

metacognitive learning to learn.

4 CONCLUSION AND OUTLOOK

Implementing a learner dashboard including crite-

rion-referenced feedback in an ILTS showed that

practice-oriented ICALL and task orientation can be

combined in a formal educational framework. Exer-

cises to practice specific language means can be pre-

sented meaningfully, highlighting their connection to

the target tasks and, thus, communicative learning

goals. Our approach can be adapted to a variety of

systems dealing with language learning, as language

means could, for example, also correspond to “Can-

Do-Statements” of the Common European Frame-

work of Reference for Languages (CEFR) standard

(Council of Europe, 2022).

A close collaboration between system developers,

educational researchers, SLA experts, English didac-

tics and practitioners was necessary so that, finally,

the learner dashboard could meet the high standards

of SLA in formal secondary education. The embed-

ding of exercises in a meaningful context (here by

structuring the ILTS based on task cycles) and other

feedback features (such as the criterion-referenced

stacked bar chart or trophy) described in this article,

can also be adapted to learner dashboards within

ILTS for other school subjects such as math.

Keeping the complexity of a learner dashboard at

a minimal level while considering didactic prerequi-

sites and user experience has been one of the major

challenges for our endeavour. We aimed to find a bal-

ance between showing learners all insights they need

to improve, and not overloading them with infor-

mation. A limitation of the learner dashboard is the

rather complicated definition of ready-to-go-ness.

Students might struggle, for example, to understand

how to earn a trophy and what it means to earn a tro-

phy in relation to the target task. Another limitation

might be that the aggregated dashboard view only de-

picts the best-performed exercise per language mean

and difficulty, which does not necessarily reveal stu-

dents’ actual development. A future version of the

dashboard could entail a single, but more elaborate,

combined criterion-referenced performance score

that considers all recent core and parallel exercise

submissions. A “forgetting mechanism” considering

only most recent submissions should be included in

Towards Task-Oriented ICALL: A Criterion-Referenced Learner Dashboard Organising Digital Practice

675

such a performance score. Such a mechanism would

require many parallel exercises but allow students to

“polish” their dashboard through more practice. We

introduced a dual-purpose dashboard that combines

exercise navigation and selection with an expandable

task-oriented progress and performance view. In the

dashboard, we visualised the pre-task activities, or-

ganised by task requirements (the language means),

as stepping stones towards successfully fulfilling the

target task. Therefore, we introduced three new con-

cepts:

• An easy-to-understand criterion-referenced

exercise performance feedback (including

“correct at first try”)

• Parallel exercises for further practice

• Operationalisation of acquisition criteria via

mastery criterion (ready-to-go-ness)

Realising this prototype of a criterion-referenced

learner dashboard is only the start for further research

on combining task orientation and ICALL. The avail-

ability of a task-supported learning ILTS for seventh-

grade English learners evokes the question of how its

actual field use impacts students’ learning and in-

class participation: Does the additional, intensive pre-

task phase when students practice with the FeedBook

help students to solve the in-class target task more

successfully? Does the criterion-referenced feedback

influence students’ motivation or perceived compe-

tence? And ultimately, is the criterion-referenced

dashboard beneficial for students’ language acquisi-

tion and proficiency?

To answer these questions, the FeedBook, includ-

ing the criterion-referenced learner dashboard, was

used in a multi-site cluster-randomised controlled

field trial in the context of the Interact4School project

(see Parrisius et al., 2022). Among other variations,

classes participating in the study received either ac-

cess to the entire criterion-referenced learner dash-

board or a control version without the performance

bar when working with the FeedBook. The use of the

ILTS and dashboard throughout the whole school

year was accompanied by several data collections fo-

cusing on assessing students’ current language com-

petencies and motivation. First investigations showed

that students performed better in task cycle-specific

performance tests if they had access to the learner

dashboard compared with students who had no access

(Parrisius, Wendebourg, Rieger, et al., 2024). Further,

if students reported high initial motivation for Eng-

lish, they experienced positive effects of the learner

dashboard on their subsequent motivation (Parrisius,

Wendebourg, Pieronczyk, et al., 2024).

Other follow-up projects will focus on mainly two

further aspects. First, to address heterogeneity, the

task-oriented dashboard will be extended and adapted

for adaptive exercise sequencing. Second, so far,

teachers have limited access to the performance-

based information the students receive through the

dashboard. In a next step, we will examine how to

best aggregate the learners’ information on a class

level in the form of a teacher dashboard and how such

a dashboard impacts teaching decisions.

ACKNOWLEDGEMENTS

We thank Kristina Dawidowsky from the University

of Tübingen for her expertise and contribution in the

field of user interface design and user experience. We

also thank Carolyn Blume from the TU Dortmund,

Diana Pili-Moss, and Torben Schmidt from the

Leuphana University Lüneburg for their project co-

operation and creation of exercises for the FeedBook.

This research is based on work carried out in the pro-

ject Interact4School which is funded by the German

Federal Ministry of Education and Research (BMBF

01JD1905A & 01JD1905B). The authors are respon-

sible for the content of this publicatio

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