Conference PaperPDF Available

Learning Analytics and Spelling Acquisition in German – Proof of Concept



German orthography is known to be quite difficult to master, especially for primary-school pupils in writing texts [cf. 1]. In order to support children with the acquisition of German orthography, we are developing a web-based platform for German-speaking users based on learning analytics techniques. Our goal is to motivate pupils age 8 to 12 to improve their spelling abilities by writing texts and by the possibility to publish them. Concerning spelling in combination with learning analytics the system provides - in case of an orthographic mistake - a specific feedback that encourages pupils to think about the spelling and to correct it. Based on occurred mistakes the teachers and the students are provided with a qualitative analysis of the mistakes. This analysis shows the problematic orthographic areas and gives suggestions for online and offline exercises as well as online courses that are explaining the orthographic phenomena. The aim of this article is to describe the architecture of the web-based system and a proof of concept by evaluating 60 essays. Furthermore, relevant background information is given in order to gain a better understanding in the complex interdisciplinary development.
Draft originally published in: Ebner M., Edtstadler K., Ebner M. (2017) Learning Analytics
and Spelling Acquisition in German Proof of Concept. In: Zaphiris P., Ioannou A. (eds)
Learning and Collaboration Technologies. Technology in Education. LCT 2017. Lecture Notes
in Computer Science, vol 10296. pp. 257-268 Springer, Cham
adfa, p. 1, 2011.
Learning Analytics and Spelling Acquisition in German
Proof of Concept
Markus Ebner1, Konstanze Edtstadler2, and Martin Ebner3
1Institute of Interactive Systems and Data Science,
Graz University of Technology, Graz, Austria
2Institute of Professionalization in Early Childhood and Primary Teacher Education,
University of Teacher Education Styria, Graz, Austria
3Department of Educational Technology,
Graz University of Technology, Graz, Austria
Abstract. German orthography is known to be quite difficult to master, espe-
cially for primary-school pupils in writing texts [cf. 1]. In order to support chil-
dren with the acquisition of German orthography, we are developing a web-
based platform for German-speaking users based on learning analytics tech-
niques. Our goal is to motivate pupils age 8 to 12 to improve their spelling
abilities by writing texts and by the possibility to publish them. Concerning
spelling in combination with learning analytics the system provides - in case of
an orthographic mistake - a specific feedback that encourages pupils to think
about the spelling and to correct it. Based on occurred mistakes the teachers and
the students are provided with a qualitative analysis of the mistakes. This analy-
sis shows the problematic orthographic areas and gives suggestions for online
and offline exercises as well as online courses that are explaining the ortho-
graphic phenomena. The aim of this article is to describe the architecture of the
web-based system and a proof of concept by evaluating 60 essays. Furthermore,
relevant background information is given in order to gain a better understanding
in the complex interdisciplinary development.
Keywords: German orthography · qualitative analysis of misspellings · Tech-
nology Enhanced Learning · Educational media
1 Introduction
This article presents a first proof of concept of the prototype platform from the project
“IDeRBlog”, which is an acronym of German Individuell Differenziert Richtig
schreiben mit Blogs, which means translated literally to English "Individually differ-
entiated correctly writing by using blogs". The project combines Technology En-
hanced Learning (TEL) with Learning Analytics (LA) in the context of German or-
thography and spelling acquisition [2].
The IDeRBlog system provides a platform for children aged between 8 and 12
years. On this platform they can write and submit essays about their daily business or
specified topics, which are assigned or proposed by the teacher. Teachers then can
review the texts on the platform, correct them, give feedback and hand it back to the
students for further inspection. Before students hand in the text, the system offers the
users the possibility to check their spelling with the help of our "intelligent diction-
ary". In case of a beforehand categorized mistake the systems gives a feedback that
encourages the user to think about the spelling by applying a strategy in order to cor-
rect the mistake. In contrast to a conventional auto correction system which only pro-
vides information that the word is (possibly) wrong and may suggest the correct word
- or a list of possibly words - our systems helps to gain deeper insight in the system of
German orthography. The correct spelling of a word - without a strategy based feed-
back - is presented for only very few words. These are words that cannot be explained
systematically by the system of the German orthography and need to be memorized.
This way of supporting children by giving them a specific feedback when correcting
texts is supposed to lead to a deeper understanding of the German orthography and its
complex system.
The prototype is web-based due to the increasing use of devices such as computers
and laptops as well as mobile devices with internet connection [3]. Thereby it is pos-
sible to trace interactions [4] between students and the learning platform for later
analysis. Another benefit of this approach is the attractiveness of writing with com-
puters for children [5]. The provided blog further gives reasons for writing because
the pupils can publish their essays later on [6]. Therefore, we expect a higher motiva-
tion in formulating and revising a text in contrast to typical essay writing in a class-
room [7].
Through this platform we suppose to gain insights into a learners’ learning process
[8] for early detection of learning issues. Teachers then can use this information to
intervene [9, 10] and help pupils with the acquisition of German orthography. There-
fore, the platform provides an area for teachers where they can correct and prepare the
texts for publishing in the blog as well as write further feedback to the student.
1.1 Research Questions
In this article we will answer the following research questions to provide a proof of
Does this first test proof our concept of the system?
How many spelling mistakes will be recognized by our intelligent dictionary?
Which are the mistakes the system cannot identify?
Which teacher categoriesare used most frequently?
1.2 Outline
The next section provides background about the German orthography, the intelligent
dictionary and its system of categories. Further, the concept and the technology of the
platform will be discussed. The third section will provide the results of the first test
with texts provided by students. The last section discusses our findings, limitations
and future work on this project.
2 Background
2.1 German orthography
The German orthography is much more transparent than the English one, where “the
alphabet contains just 26 letters [which] correspond to 44 phonemes associated with
102 functional spelling units” [11]. Nevertheless, it is not as transparent as, for exam-
ple, the Turkish one. Therefore, a lot of words - but not all - cannot be spelled by
relying on the phoneme-grapheme-correspondences since other orthographic princi-
ples are interfering. For example, the German word for hat <Hut> can be spelled cor-
rectly by relying on the phoneme-grapheme-correspondences, whereas the German
word for dog <Hund> would be spelled incorrectly by purely relying on the corre-
spondences. The reason for this is the so called phenomenon of terminal devoicing
and the existence of the morphological principle. Because of these phenomena the
word is pronounced as /hunt/ which would lead to the incorrect spelling <*Hunt>. At
the beginning of a syllable the obstruent is pronounced voiced, as in /hundə/. Conse-
quently, the spelling of <Hund> is due to the morphological principle of the German
orthography. Because of this principle morphemes and words are spelled the same
way in all possible words (e.g. <Hund, Hunde, Hündin>, not <*Hunt, Hunde, Hün-
din>). Furthermore, the German orthography uses capitalization not only at the begin-
ning of a sentence, but also within a sentence in order to mark substantives. There-
fore, the reason for the correct spelling <Hund> in contrast to the incorrect spelling
<*hund> lies in the syntactic principle.
The co-existence of these principles, which are described above in a very brief and
superficial way (for a detailed description see e.g. [12]), often leads to the assumption
that the German orthography is unsystematic and illogical. One possible consequence
is, that children are confronted with an unsystematic way of instruction that focuses
on learning by rote. Although the mastery of the orthography is rather important in
German, since it is rather prestigious, students experience spelling instructions as
boring and formal [13]. “In contrast to other areas of language learning, there is hard-
ly space to argue about the correct or incorrect spelling of a word. This orthographical
stiffness can probably serve as an explanation for its importance” [7].
2.2 Intelligent dictionary
The main idea is to improve the orthographic competence of pupils by writing essays
on a platform that provides a special feature - the intelligent dictionary - which gives a
feedback in order to think about and consequently correct the misspelled word. It
offers the correct spelling only in a few selected cases. Unlike a conventional auto
correction system the intelligent dictionary in general does not offer the correctly
spelled word straightaway in order to serve didactic purposes: First, students have to
give attention to the feedback and have to process it by applying it on the misspelled
word. Second, this approach is based on a wider definition of spelling competence
that does not only include a person´s knowledge of the correct spelling of given words
and the rules of orthography, but also being sensitive to misspelled words, knowing
how to correct them, and applying strategies to prevent spelling errors in a long run
[7, 14] Third, this system follows a modern approach of teaching and learning orthog-
raphy, which considers the communicative aspect of writing (cf. e.g. [15]): The pupils
work on their orthographic competence by writing essays which can also be pub-
lished. Therefore, the motivation to correct the mistakes should be higher and it might
be more attractive than doing conventional exercises that focus purely on orthogra-
phy. Nevertheless, the platform offers online exercises and printable worksheets in
order to work on a specific orthographic phenomenon. Although orthography is only
one aspect among others of text writing skills, it is an important one to work on. This
shows a big survey of various competences in German language including also read-
ing and listening among others: The results indicate that 27% of the tested Austrian
pupils in grade 4 did not reach the standards of the application of the correct orthog-
raphy and punctuation in the task of producing texts (cf. [1]).
2.3 Categorization
In order to give a feedback for correcting the mistakes and in order to offer a qualita-
tive analysis of the mistakes it is necessary to establish a complex system of catego-
ries. This system is developed on a linguistic and orthographic basis. Currently the
systems covers 28 categories, separated into 143 phenomena and 58 feedbacks.
The reason for these unequal numbers lies in the different requirements: The cate-
gories are visible for the users in the qualitative analysis. Therefore, the number
should be kept as small as possible, but as exact as necessary. These 28 categories are
also labeled "teacher categories", since especially the teachers will work with them.
Due to the complexity of German orthography the possible mistakes must be divided
in different phenomena in order to categorize the misspelled words in an exact way
for constructing the intelligent dictionary. In the system each phenomenon is connect-
ed with a category. For the feedback it is possible to merge two or even more phe-
nomena. This helps to keep the amount of different feedbacks as small as possible in
order that the users get familiar with the different hints. Considering the requirements
of a scientific analysis the fine-grained phenomena allow a deep analysis in order to
gain a better understanding of the acquisition process in a long run. Consequently, it
could be necessary to add new phenomena or delete existing ones, which is easier to
manage due to the level of phenomena.
To gain a better understanding of this system, an example of a category with its
phenomena and feedback is given: The category "prefix" consists of 12 phenomena.
Due to the morphological principle of German orthography a prefix is always spelled
the same way in all possible words and word forms with this certain prefix. For ex-
ample, the prefix ver- is always spelled as <ver> like in verlaufen (to lose ones ways),
verlieben (to fall in love), verreisen (to go on a journey) and the prefix ent- is always
spelled as <ent-> in entdecken (to discover) or entfernen (to remove). Each of the 12
phenomena of this category describe one prefix with its possible mistakes (e.g. <*fer>
instead of <ver> in <*ferreisen> or <*end> instead of <ent> in <*enddecken>). Since
spelling errors of this kind are very similar, the same feedback can be given for all 12
phenomena. Therefore, the pupils get the (literally translated) feedback "Think about
the spelling of the world building brick". This should guide the writer's attention to-
wards the prefix and enable him/her to correct it.
The advantages of the linking of the different phenomena with one category are the
following: First, this enables us to conduct analysis of each phenomenon separately in
order to gain a better understanding of the use and frequency of spelling mistakes of
each prefix. Second, we can add phenomena for prefixes that are not considered yet.
Therefore, modifications concerning the phenomena can be undertaken without con-
fusing the user.
Since the lexicon of a language is endless, it is not - and will never be - possible to
consider all words of a language and all possible mistakes of a specific word. There-
fore, the development of the intelligent dictionary is currently based on the words of
the basic vocabulary of three federal states in Germany (for details see [7]). For these
words all word forms are considered. This is challenging especially in the German
language since it has quite a rich morphology. The number of word forms for one
word varies from one word form (e.g. prepositions) up to 17 different word forms
(e.g. adjectives).
Based on this word forms the possible mistakes are derived and assigned to a phe-
nomenon. Therefore, one word form can be connected with different misspelled
words in different phenomena.
2.4 Platform
With the IDeRBlog platform we try to combine the development of writing skills,
acquisition of orthographic competence and improving the reading skills with modern
means of communication and digital instruments [7].
ADD Fig 1. here
Fig. 1. Architecture.
Fig. 1 shows the IDeRBlog system, which can be used after prior registration with a
separate user management system. It is a web-based application with state of the art
technology such as HTML5, responsive web design and web services for native An-
droid or iOS applications (under development). The Application Server handles the
communication from the students and the teachers and is implemented with the
GRAILS web application framework for Java platforms. Grails is based on Groovy
and uses different established frameworks such as Spring and Hibernate. To ensure a
clean and manageable project the Model View Controller (MVC) Pattern is used.
The submitted text by the student is first analyzed automatically regarding spelling
mistakes. Here we use the conventional system of dividing the text into sentences and
further into tokens. After the part-of-speech tagging [16] the tokens are assigned to
categories. Based on that information our intelligent dictionary will provide age-
appropriate feedback, according to the detected spelling mistake in connection with
its phenomenon. As described above, the feedback is designed to encourage students
to reflect and think about the made spelling mistakes and become aware of the struc-
ture of the words [7, 20]. Additionally, spelling mistakes which have not been catego-
rized by the intelligent dictionary will be marked as spelling mistakes without a spe-
cific feedback. Further, based on the occurred errors and its corresponding categories,
the platform can recommend exercises from the provided training database [17]. In
order to understand, how this systems works in practice, Fig. 2 shows the feedback for
two different mistakes:
ADD Fig. 2 here
Fig. 2. Text correction example
Fig. 2 shows a feedback example with the text, which means in English, „Today we
discovered many new things in the woods. The distance between our camp and the
river was very far”. The student made two quite similar spelling mistakes: enddeckten
('(we) discovered') with <*end> instead of <ent> and Endfernung ('(the) distance')
with <*End> instead of <Ent>, which are shown to him/her with the appropriate hint
for correction: “Think about the spelling of the world building brick”. As described in
the background section, the writer’s attention should be guided to the prefix and ena-
ble him/her to correct it. The headline serves as an instruction as it tells that pupils,
that he/she can see his/her mistakes and that he/she gets hints for correcting them. The
hints appear, when the pupils clicks on or hover over the highlighted word.
This intelligent dictionary is embedded in a platform that offers more features and has
a specific workflow for pupils and teachers, as shown in Fig. 3 and described sepa-
rately for pupils' and teachers' use.
Workflow for pupils
After login the pupils have several possibilities: They can start to write a new text
in the writing area (1) or access the reports of their previously submitted texts and the
evaluation carried out by the teacher; they can access the private/class/school-blog,
where they can find published texts of other pupils, or they can work on recommend-
ed exercises in the training database.
In case they start to write a new text, this text will be analyzed orthographically by
the intelligent dictionary in a first step (2) [7]. Proper feedback, based on the spelling
mistake and the category, will be displayed to the student - as shown in Fig. 2. In this
phase, he/she can continue to correct the text (3) which supports the self-reflexivity of
spelling mistakes by trying to correct them independently [18] and finally submit the
text to the teacher (4). After the correction by the teacher, the student is informed
about the report (7) or the necessity to redo the text writing (7a), then the process
starts again (1). If the teacher has finished the review and correction of the essay, the
pupil can blog the text in one of the three available blogs (8). Further, based on the
evaluation of the texts, exercises are recommended to the student for self-learning (9).
Workflow for teachers
As soon as the pupil submitted a text, the teacher gets a notification (5). The teach-
er can correct the submitted text within the platform concerning various aspects and
add a personalized feedback. In the next step the teacher can either let the student edit
the text according to the given feedback in order to resubmit his/her text again (7a) or
make the final reviewed version available in the students’ area (7). Concerning the
orthographic competence of a specific pupil or the class in general, the teacher can
inspect the performance according to the qualitative analysis and decide to assign
spelling exercises to the pupil and/or class (10).
Fig. 3. Workflow for students and teachers.
3 Results
Since the platform will be used by schools in the course of 2016/2017 our initial re-
search aims to proof our concept of the system. Since there are so many possibilities
to spell a word incorrectly it is important to test the system with authentic mistakes
from authentic texts written by pupils of our target group. In order to conduct this
analysis, we collected 60 essays written by students of 3rd grade, aged around 8 years
within the project group. These texts are digitized and made anonymous.
3.1 Findings
The collected essays contain 405 sentences with 3792 tokens (words and punctuation
marks). The amount of characters is 19237 including white space (15694 without
white space). In the collected essays 549 spelling mistakes can be found. Our intelli-
gent dictionary responded to 95 of these 549 spelling mistakes with the appropriate
feedback. Currently our intelligent dictionary covers 17.3% of the total found spelling
mistakes in the 60 essays.
The top 5 categories of the analysis based on the intelligent dictionary are i) "gem-
ination" (which means that only one consonant instead of two is spelled, e.g.
<*gesamelt> instead of <gesammelt>“ 'collected'), ii) "complex graphemes“ (which
means, that more than one letter is necessary for spelling one phoneme, e.g.
<*speilen> instead of <spielen> 'to play'), iii) „use of lower case letters instead of
upper case letters (e.g. <*buch> instead of <Buch> 'book'), iv) „spelling of the s-
sound" (e.g. <*weis> instead of <weiß> 'white'), „word to memorize" (this category
contains words, that cannot be spelled correctly by applying a strategy, e.g. <*unt>
instead of <und> 'and').
Table 1 shows the top 5 categories and the number of spelling mistake occurrences
and percentage over all analyzed essays within the intelligent dictionary:
Table 1. Top 5 categories.
complex graphemes
use of lower case letters instead of upper case letters
spelling of the s-sound
word to memorize
Those top 5 categories cover 72.7% of the found spelling mistakes from our intel-
ligent dictionary (see Fig. 4). Since the category "complex graphemes" contains also
missing dieresis (e.g. <u> instead of <ü>), which is a common mistake in handwrit-
ing, this category probably will not reach such a top place when children are typing
on a keyboard because all German letters that require dieresis (<ä, ü, ö>) are repre-
sented on the keyboard. Therefore, this phenomenon should not occur very often,
whereas the phenomenona of <ie> instead of <ei> and/or <ei> instead of <ie>, that
belong also to the same category are likely to happen. Problems in the field of capital-
ization are, like problems with spelling the different s-sounds, very common in the
acquisition process.
ADD Fig 4. here
Fig. 4. Top 5 categories.
Of course it was expected that not all mistakes are recognized by the intelligent
dictionary. After the first proof of concept it is possible to describe these constraints
First, some categories and/or phenomena are considered in the system, but the
spelling mistakes need to be collected on basis of the written texts of the users. This is
especially true for the use of English words in German texts (e.g. <*Capten> for
<Captain>) and for names (e.g. <*Nickolaus> for <Nikolaus>).
Second, some spelling mistakes need to be analyzed by hand because mistakes
concerning phoneme-grapheme-correspondences cannot be considered, since there
are endless possibilities of disregarding the grapheme-phoneme-correspondences. As
a consequence, the intended word is only recognizable due to the context because
graphemes are for example either missing (e.g. <*Kunt> for <Kunst> 'art') or in the
wrong position (e.g. <*Geschneke> for <Geschenke> 'presents'). This kind of mis-
takes will be collected, but it is not possible to systematically categorize them in ad-
vance in order to give a feedback.
Third, since the intelligent dictionary works with a limited selection of word forms
and their corresponding mistakes, new words that pupils are using frequently should
be added systematically to the system, e.g. <*hausaufgabe> for <Hausaufgabe>
Fourth, a challenging task will be to teach the system that some words are spelled
correctly, although the dictionary does not recognize it as a correct word, because the
word is newly coined, e.g. <Partyhütchen> ('party hats').
4 Discussion and conclusion
In this study we described the concept and system behind the project IDeRBlog and
its workflow for student and teachers. A first evaluation with texts from students are
showing promising results for future evaluations and enhancements for the intelligent
dictionary. Our findings indicate that the categorized mistakes are corresponding with
the mistakes children actually make when writing texts. This is an important finding
since the categorization of mistakes is based on a complex systems of phenomena and
categories. Due to the proven stability of the system, new words and mistakes can be
added to the system in order to make the intelligent dictionary more powerful and to
gain significant analysis in future.
The implementation of this system in schools has great advantages for teachers and
pupils: First, the teacher gets easily a qualitative analysis of the spelling errors of
his/her pupils. Until now qualitative analysis of spelling mistakes of essays need to be
done by hand. This is a time consuming process that also requires a lot of knowledge
(for details see [19]). Second, based on the results of the qualitative analysis the
teachers know which orthographic areas are the most problematic ones of a specific
pupil or of a whole group. By using the platform and the possibility of retrieving a
qualitative analysis of the number and percentage of spelling mistakes per category
the teachers are supported in planning their orthography classes. Furthermore, the
system can be used for evaluating the progress of pupils in acquiring the German
orthography in general or in acquiring specific categories of the German orthography.
The advantage for the pupils is that they can improve their spelling in an attractive
digital environment that is based on scientific finding. Therefore, the platform is a
trend-setting development and application in the field of E-Learning and learning
analytics with methodology in a certain subject - namely German orthography.
The system also has big advantages for researchers in spelling acquisition: It will
be the first time that analysis of the used words and their spelling errors are possible.
This can have a huge impact on understanding acquisition processes and consequently
modifications of teaching and learning approaches.
Although there are many promising advantages, there is also a drawback: The ad-
vantages can only be considered if a big community is using the system frequently
because analysis for pupils can only be carried out in case there are enough correctly
and incorrectly spelled words. This aspect also affects the impact of the interpretation
of this preliminary findings. The data basis is limited to 60 short texts from 3rd grad-
ers. Therefore, the presented findings show the possibilities of this system, but no real
empirical evidence. Since our system is developed for pupils aged from 8 to 12 years,
we should also add texts from 4th to 6th graders. The more schools and classes will
use the system, the deeper will be the insight in the spelling process and orthographic
competence of German speaking users. This is expected to happen in the course of
2016/17 when the whole systems is offered to the public.
In order to improve the intelligent dictionary for the users, the system should grow
by adding words and their word forms as well as their possible mistakes based on the
texts written by the pupils. Further we plan to predict the performance of students,
make personalized recommendations for exercises provided by our platform and
benchmark the performance of the student’s progress in spelling acquisition.
Acknowledgments. This research project is supported by the European Commission
Erasmus+ program in the framework of the project IDeRBlog under grant VG-SPS-
SL-14-001616-3. For more information about the project IDeRBlog and its project
partners: from Germany: Gros, M., Adolph, H., Steinhauer, N. (LPM Saarland1);
Biermeier, S., Ankner, L. (Albert-Weisgerber-Schule, St. Ingbert2); from Belgium:
Huppertz, A., Cormann, M. (GS Raeren3); from Austria: Ebner, M., Taraghi, B., Eb-
ner, M. (TU Graz4); Gabriel, S., Wintschnig, M. (KPH Wien/Krems5); Aspalter, Ch.,
Martich, S., Ullmann, M. (PH Wien6); Edtstadler, K. (PH Steiermark7, before: KPH
Wien/Krems), please visit our homepage (German language only).
1 LPM Saarland, Beethovenstraße 26, 66125 Saarbrücken, Germany
2 Albert-Weisgerber School St. Ingbert, Robert-Koch-Straße 4, 66386 St. Ingbert, Germany
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4 Graz University of Technology, Department Educational Technology, Münzgrabenstraße 35a,
8010 Graz, Austria
5 University College of Teacher Education Vienna/Krems, Mayerweckstraße 1, 1210 Vienna,
6 University of Teacher Education Vienna, IBS/DiZeTIK, Grenzackerstraße 18, 1100 Vienna,
7 University of Teacher Education Styria, Hasnerplatz 12, 8010 Graz, Austria
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... The text in English means: "Today we discovered many new things in the woods. The distance between our camp and the river was very far" [19]. The student made two spelling mistakes: enddeckten (translated: (we) discovered) with <*end> instead of <ent> and Endfernung (translated: (the) distance) with <*End> instead of <Ent>, which are shown to the student with the appropriate hints for correction: "Think about the spelling of the world building brick" [19]. ...
... The distance between our camp and the river was very far" [19]. The student made two spelling mistakes: enddeckten (translated: (we) discovered) with <*end> instead of <ent> and Endfernung (translated: (the) distance) with <*End> instead of <Ent>, which are shown to the student with the appropriate hints for correction: "Think about the spelling of the world building brick" [19]. ...
... Example of a text correction[19]. ...
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This paper shows how Learning Analytic Methods are combined with German orthography in the IDeRBlog-project ( After a short introduction to the core of the platform – the intelligent dictionary – we focus on the presentation and evaluation of a new training format. The aim of this format is, that pupils can train misspelled words individually in a motivating and didactic meaningful setting. As a usability test was run with twenty one third graders, we are able to present the results of this evaluation.
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According to the NMC Horizon Report (Johnson et al. in Horizon Report Europe: 2014 Schools Edition, Publications Office of the European Union, The New Media Consortium, Luxembourg, Austin, 2014 [1]), data-driven learning in combination with emerging academic areas such as learning analytics has the potential to tailor students’ education to their needs (Johnson et al. 2014 [1]). Focusing on this aim, this article presents a web-based (training) platform for German-speaking users aged 8–12.Our objective is to support primary-school pupils—especially those who struggle with the acquisition of the German orthography—with an innovative tool to improve their writing and spelling competencies. On this platform, which is free of charge, they can write and publish texts supported by a special feature, called the intelligent dictionary. It gives automatic feedback for correcting mistakes that occurred in the course of fulfilling a meaningful writing task. Consequently, pupils can focus on writing texts and are able to correct texts on their own before publishing them. Additionally, they gain deeper insights in German orthography. Exercises will be recommended for further training based on the spelling mistakes that occurred. This article covers the background to German orthography and its teaching and learning as well as details concerning the requirements for the platform and the user interface design. Further, combined with learning analytics we expect to gain deeper insight into the process of spelling acquisition which will support optimizing our exercises and providing better materials in the long run.
Conference Paper
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Data-driven learning in combination with emerging academic areas such as Learning Analytics (LA) has the potential to tailor students’ education to their needs [1]. The aim of this article is to present a web-based training platform for primary school pupils who struggle with the acquisition of German orthography. Our objective is the improvement in their writing and spelling competences. The focus of this article is on the development of the platform and the details concerning the requirements and the design of the User Interface (UI). In combination with Learning Analytics, the platform is expected to provide deeper insight into the process of spelling acquisition. Furthermore, aspects of Learning Analytics will help to develop the platform, to improve the exercises and to provide better materials in the long run.
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Many pupils struggle with the acquisition of the German orthography. In order to meet this struggle a web based platform for German speaking countries is currently developed. This platform aims to motivate pupils aged 8 to 12 to improve their writing and spelling competences. In this platform pupils can write texts in the form of blog entries concerning everyday events or special topics. Since the core of this platform consists of an intelligent dictionary focussing on different categories of misspellings, students can improve their own spelling skills by trying to correct their mistakes according to the feedback of the system. Teachers are informed about specific orthographic problems of a particular student by getting a qualitative analysis of the misspellings from this intelligent dictionary. The article focuses on the development of the intelligent dictionary, details concerning the requirements, the categorization and the used wordlist. Further, necessary information on German orthography, spelling competence in general and the platform itself is given. By implementing methods of learning analytics it is expected to gain deeper insight into the process of spelling acquisition and thus serves as a basis to develop better materials on the long run.
Conference Paper
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Within the evolution of technology in education, Learning Analytics has reserved its position as a robust technological field that promises to empower instructors and learners in different educational fields. The 2014 horizon report (Johnson et al., 2014), expects it to be adopted by educational institutions in the near future. However, the processes and phases as well as constraints are still not deeply debated. In this research study, the authors talk about the essence, objectives and methodologies of Learning Analytics and propose a first prototype life cycle that describes its entire process. Furthermore, the authors raise substantial questions related to challenges such as security, policy and ethics issues that limit the beneficial appliances of Learning Analytics processes.
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Nell’era di Internet, delle tecnologie mobili e dell’istruzione aperta, la necessità di interventi per migliorare l’efficienza e la qualità dell’istruzione superiore è diventata pressante. I big data e il Learning Analytics possono contribuire a condurre questi interventi, e a ridisegnare il futuro dell’istruzione superiore. Basare le decisioni su dati e sulle evidenze empiriche sembra incredibilmente ovvio. Tuttavia, l’istruzione superiore, un campo che raccoglie una quantità enorme di dati sui propri “clienti”, è stata tradizionalmente inefficiente nell’utilizzo dei dati, spesso operando con notevole ritardo nell’analizzarli, pur essendo questi immediatamente disponibili. In questo articolo, viene evidenziato il valore delle tecniche di analisi dei dati per l’istruzione superiore, e presentato un modello di sviluppo per i dati legati all’apprendimento. Ovviamente, l’apprendimento è un fenomeno complesso, e la sua descrizione attraverso strumenti di analisi non è semplice; pertanto, l’articolo presenta anche le principali problematiche etiche e pedagogiche connesse all’utilizzo delle tecniche di analisi dei dati in ambito educativo. Cionondimeno, il Learning Analytics può penetrare la nebbia di incertezza che avvolge il futuro dell’istruzione superiore, e rendere più evidente come allocare le risorse, come sviluppare vantaggi competitivi e, soprattutto, come migliorare la qualità e il valore dell’esperienza di apprendimento.
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With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning and Knowledge Analytics as well as the consequences for learning and teaching are still far from being understood. In this paper, we explore the key dimensions of Learning Analytics (LA), the critical problem zones, and some potential dangers to the beneficial exploitation of educational data. We propose and discuss a generic design framework that can act as a useful guide for setting up Learning Analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. Furthermore, the presented article intends to inform about soft barriers and limitations of Learning Analytics. We identify the required skills and competences that make meaningful use of Learning Analytics data possible to overcome gaps in interpretation literacy among educational stakeholders. We also discuss privacy and ethical issues and suggest ways in which these issues can be addressed through policy guidelines and best practice examples.
Even though Specific Reading Disability (Dyslexia) has been clinically recognized as a developmental learning disorder for nearly a hundred years. only within the past two decades it has become the subject of major experimental investigation. Because. by definition. dyslexic children are of average or superior intelligence. it is often suspected that some arcane feature of the written language is responsible for the inordinate difficulty experienced by these children in learning to read. The occasional claim that developmental dyslexia is virtually nonexistent in some languages coupled with the fact that languages differ in their writing systems has further rendered orthography a subject of serious investigation. The present Volume represents a collection of preliminary reports of investigations that explored the relationship between orthography and reading disabilities in different languages. Even though not explicitly stated. these reports are concerned with the question whether or not some orthographies are easier to learn to read and write than others. One dimension on which orthographies differ from each other is the kind of relationship they bear to pronunciation. The orthographies examined in this book range from the ones that have a simple one-to­ one grapheme-phoneme relationship to those which have a more complex relationship.
This article outlines the recently used methods for designing part-of-speech taggers; computer programs for assigning contextually appropriate grammatical descriptors to words in texts. It begins with the description of general architecture and task setting. It gives an overview of the history of tagging and describes the central approaches to tagging. These approaches are: taggers based on handwritten local rules, taggers based on ngrams automatically derived from text corpora, taggers based on hidden Markov models, taggers using automatically generated symbolic language models derived using methods from machine tagging, taggers based on handwritten global rules, and hybrid taggers, which combine the advantages of handwritten and automatically generated taggers. This article focuses on handwritten tagging rules. Well-tagged training corpora are a valuable resource for testing and improving language model. The text corpus reminds the grammarian about any oversight while designing a rule. © Editorial matter and organization Ruslan Mitkov 2003. All rights reserved.
In spite of the complexities of written language systems, the majority of children learn to read easily. However, a minority have difficulty acquiring literacy skills even though they master other tasks well. These children are sometimes described as dyslexic. This article presents current scientific understanding of dyslexia from a developmental perspective. First, it considers the definition of dyslexia in behavioural terms and, with the normal development of literacy as a framework, discusses how its manifestation differs according to the language in which the child is learning to read. The article then examines cognitive explanations of dyslexia and evidence concerning sensory, biological, and environmental factors in its aetiology; behavioural manifestations of dyslexia (dyslexia in English, dyslexia in consistent orthographies, dyslexia in Chinese); theories of dyslexia (cognitive theories, automatization deficit hypothesis, auditory processing impairments, visual processing deficits, speech perception); and reading intervention programs. It concludes by looking at how theoretical advances in the field of dyslexia provide the rationale for effective interventions.