Augmented intelligence in educational data mining
Abstract Educational data mining (EDM) processes have shifted towards open-ended processes with visualizations and parameter and predictive model adjusting. Data and models in hyperdimensions can be visualized for end-users with popular data mining platforms such as Weka and RapidMiner. Multiple stu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2019-09-01
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| Series: | Smart Learning Environments |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40561-019-0086-1 |
| _version_ | 1819009924886167552 |
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| author | Tapani Toivonen Ilkka Jormanainen Markku Tukiainen |
| author_facet | Tapani Toivonen Ilkka Jormanainen Markku Tukiainen |
| author_sort | Tapani Toivonen |
| collection | DOAJ |
| description | Abstract Educational data mining (EDM) processes have shifted towards open-ended processes with visualizations and parameter and predictive model adjusting. Data and models in hyperdimensions can be visualized for end-users with popular data mining platforms such as Weka and RapidMiner. Multiple studies have shown how the adjusting and even creating the decision tree classifiers help EDM end-users to better comprehend the dataset and the context where the data has been collected. To harness the power of such open-ended approach in EDM, we introduce a novel Augmented Intelligence method and a cluster analysis algorithm Neural N-Tree. These contributions allow EDM end-users to analyze educational data in an iterative process where the knowledge discovery and the accuracy of the predictive model generated by the algorithm increases over time through the interactions between the models and the end-users. In contrast to other similar approaches, the key in our method is in the model adjusting and not in parameter tuning. We report a study where the potential EDM end-users clustered data from an education setting and interacted with Neural N-Tree models by following Augmented Intelligence method. The findings of the study suggest that the accuracy of the models evolve over time and especially the end-users who have a adequate level of knowledge from data mining benefit from the method. Moreover, the study indicates that the knowledge discovery is possible through AUI. |
| first_indexed | 2024-12-21T01:04:06Z |
| format | Article |
| id | doaj.art-ff03979e2ec9466ca06532e97e731b7b |
| institution | Directory Open Access Journal |
| issn | 2196-7091 |
| language | English |
| last_indexed | 2024-12-21T01:04:06Z |
| publishDate | 2019-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Smart Learning Environments |
| spelling | doaj.art-ff03979e2ec9466ca06532e97e731b7b2022-12-21T19:21:06ZengSpringerOpenSmart Learning Environments2196-70912019-09-016112510.1186/s40561-019-0086-1Augmented intelligence in educational data miningTapani Toivonen0Ilkka Jormanainen1Markku Tukiainen2School of Computing, University of Eastern FinlandSchool of Computing, University of Eastern FinlandSchool of Computing, University of Eastern FinlandAbstract Educational data mining (EDM) processes have shifted towards open-ended processes with visualizations and parameter and predictive model adjusting. Data and models in hyperdimensions can be visualized for end-users with popular data mining platforms such as Weka and RapidMiner. Multiple studies have shown how the adjusting and even creating the decision tree classifiers help EDM end-users to better comprehend the dataset and the context where the data has been collected. To harness the power of such open-ended approach in EDM, we introduce a novel Augmented Intelligence method and a cluster analysis algorithm Neural N-Tree. These contributions allow EDM end-users to analyze educational data in an iterative process where the knowledge discovery and the accuracy of the predictive model generated by the algorithm increases over time through the interactions between the models and the end-users. In contrast to other similar approaches, the key in our method is in the model adjusting and not in parameter tuning. We report a study where the potential EDM end-users clustered data from an education setting and interacted with Neural N-Tree models by following Augmented Intelligence method. The findings of the study suggest that the accuracy of the models evolve over time and especially the end-users who have a adequate level of knowledge from data mining benefit from the method. Moreover, the study indicates that the knowledge discovery is possible through AUI.http://link.springer.com/article/10.1186/s40561-019-0086-1Educational data miningMachine learningAugmented Intelligence |
| spellingShingle | Tapani Toivonen Ilkka Jormanainen Markku Tukiainen Augmented intelligence in educational data mining Smart Learning Environments Educational data mining Machine learning Augmented Intelligence |
| title | Augmented intelligence in educational data mining |
| title_full | Augmented intelligence in educational data mining |
| title_fullStr | Augmented intelligence in educational data mining |
| title_full_unstemmed | Augmented intelligence in educational data mining |
| title_short | Augmented intelligence in educational data mining |
| title_sort | augmented intelligence in educational data mining |
| topic | Educational data mining Machine learning Augmented Intelligence |
| url | http://link.springer.com/article/10.1186/s40561-019-0086-1 |
| work_keys_str_mv | AT tapanitoivonen augmentedintelligenceineducationaldatamining AT ilkkajormanainen augmentedintelligenceineducationaldatamining AT markkutukiainen augmentedintelligenceineducationaldatamining |