@inproceedings{harmsen-etal-2024-joint,
    title = "A Joint Approach for Automatic Analysis of Reading and Writing Errors",
    author = "Harmsen, Wieke  and
      Cucchiarini, Catia  and
      van Hout, Roeland  and
      Strik, Helmer",
    editor = "Gorman, Kyle  and
      Prud'hommeaux, Emily  and
      Roark, Brian  and
      Sproat, Richard",
    booktitle = "Proceedings of the Second Workshop on Computation and Written Language (CAWL) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.cawl-1.2",
    pages = "8--17",
    abstract = "Analyzing the errors that children make on their ways to becoming fluent readers and writers can provide invaluable scientific insights into the processes that underlie literacy acquisition. To this end, we present in this paper an extension of an earlier developed spelling error detection and classification algorithm for Dutch, so that reading errors can also be automatically detected from their phonetic transcription. The strength of this algorithm lies in its ability to detect errors at Phoneme-Corresponding Unit (PCU) level, where a PCU is a sequence of letters corresponding to one phoneme. We validated this algorithm and found good agreement between manual and automatic reading error classifications. We also used the algorithm to analyze written words by second graders and phonetic transcriptions of read words by first graders. With respect to the writing data, we found that the PCUs {`}ei{'}, {`}eu{'}, {`}g{'}, {`}ij{'} and {`}ch{'} were most frequently written incorrectly, for the reading data, these were the PCUs {`}v{'}, {`}ui{'}, {`}ng{'}, {`}a{'} and {`}g{'}. This study presents a first attempt at developing a joint method for detecting reading and writing errors. In future research this algorithm can be used to analyze corpora containing reading and writing data from the same children.",
}

Analyzing the errors that children make on their ways to becoming fluent readers and writers can provide invaluable scientific insights into the processes that underlie literacy acquisition. To this end, we present in this paper an extension of an earlier developed spelling error detection and classification algorithm for Dutch, so that reading errors can also be automatically detected from their phonetic transcription. The strength of this algorithm lies in its ability to detect errors at Phoneme-Corresponding Unit (PCU) level, where a PCU is a sequence of letters corresponding to one phoneme. We validated this algorithm and found good agreement between manual and automatic reading error classifications. We also used the algorithm to analyze written words by second graders and phonetic transcriptions of read words by first graders. With respect to the writing data, we found that the PCUs {`}ei{'}, {`}eu{'}, {`}g{'}, {`}ij{'} and {`}ch{'} were most frequently written incorrectly, for the reading data, these were the PCUs {`}v{'}, {`}ui{'}, {`}ng{'}, {`}a{'} and {`}g{'}. This study presents a first attempt at developing a joint method for detecting reading and writing errors. In future research this algorithm can be used to analyze corpora containing reading and writing data from the same children.
Harmsen, Wieke Cucchiarini, Catia van Hout, Roel Strik, Helmer
A Joint Approach for Automatic Analysis of Reading and Writing Errors