# A Pattern Mining Method for Teaching Practices
## 3 Experimental Results  
To demonstrate the procedures described in the previous sections, this part of the article uses generated sample data for demonstration purposes, as not enough real data were collected yet at this time. The following example uses a small sample of data from teaching to show how the association algorithm is used to identify frequencies, which in turn can be used to describe a pattern. The data describe three courses (Algorithms 2, Didactics 1, Statistics, Digital Education 1, and Analysis 1) and which digital tools and software were used during the introductory phase of lessons. Therefore, first data were retrieved from the graph database (Neo4j) using the _Cypher_ database query language for further processing. The query in Listing 1 includes a sub-procedure (CALL), which first queries the relationship between \"Seminar\" and \"Activation\" with the MATCH query. The result of this is passed via WITH to another query to the \"App\" node, and so on. At the end, the entire queries are aggregated in a list with COLLECT and passed to d via RETURN. The output of the database query is shown in Figure 6.  
``` CALL {  MATCH(s:Seminar)-[:HAS]->(sa:Activation) WITHs,sa MATCH(s)-[:USES]->(a:App) WITHs,sa,a MATCH(s)-[:INTEGRATES]->(m:MobileDevice) WITHCOLLECT({[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[



In line 9-10 in Listing 2 the metric for the rule generation using the apriori algorithm is set as _confidence_ for evaluating association rules. In line 11 a filter is set, in order to identify rules, which associate a consequent of a _high_ student activation. The output is shown in Figure 8.

Figure 6: Table output of Cypher query. Figure 7: Snippet of the one-hot representation of the data. The results in Figure 8, show frequent associations with a _high_ impact on student activation. The support of each association is >0.25 and confidence >0.66. In particular, the combination of _Padlet_,_Notebook -> high(support \(s=25\%\), confidence \(c=100\%\)) showed a particularly strong criterion for cognitive activation, which also can bee seen in the single associations of Padlet and Notebook in Figure 8. But the support of this association rule shows a low importance in this small dataset. ## 4 Interpretation of Results for Pattern Candidates

First, it must be mentioned that the following interpretations were only intended to demonstrate the procedure. No conclusions can be drawn from the small amount of data here. Therefore, the lower bound of the support was also set very low to be able to demonstrate results. To analyze and interpret these results, the four-step process of a semi-automatized pattern identification as suggested above was considered: (1) Identification of frequent associations (Apriori algorithm); (2) description of a hypothesis; (3) manual review and comparison of the lesson data; (4) description of possible pattern. From the above results, it could be also concluded that the use of a notebook with the app Padlet possibly results in higher cognitive activation in the introductory phase of the lesson (in this example of the domain digital education) as a possible hypothesis from these results. Although these interpretations are only suggestive of an effective use of technology, these results can still serve as a further basis for identifying, describing and validating a pattern. In the next step, in order to validate this hypothesis and to describe a pattern, additional information is now compiled from the available data. For example, the larger context of the use of notebooks during an introductory phase of instruction will be considered. What was the teacher's exact approach? In what sequence were the notebooks used in class? Were there any difficulties? How were these resolved?