**3.2 Phases in the GCM**

The works of Kirschner and Stoyanov [22] and Kane and Rosas [21] were consulted to determine the activities per phase (see **Table 1**). The following phases are part of GCM:

	- How important is this idea for students' study success?
	- How feasible is this idea?

The rating phase lasted also a month.

7.The researchers made additional analyses and interpreted and reported the results


#### **Table 1.**

*Overview of the activities per phase of the GCM.*

The whole project took about a year starting with formulating the focus prompt and ending with the research report.

### **4. Results**

This section presents the results on an aggregated level. For a more detailed description of the results, please consult the work of Goes-Daniëls and Van der Klink [27].

#### **4.1 The brainstorm phase: generating statements**

During the brainstorming session, a total of 893 logins were recorded, out of which 780 participants engaged with the program. Brainstorming by students and teachers led to the generation of 547 statements, which were subsequently exported to an Excel file. These statements were then evaluated by two researchers who divided them into three categories: (1). relevant and useful, (2). suggesting some relevance but not closely related to the focus prompt, and (3). irrelevant and lack of usefulness. The pool of relevant and useful statements (331 in total) needed a reduction to a maximum of 100 statements to maintain participant engagement and ensure an adequate response rate for the sorting and rating activities. To achieve this, one researcher assigned keywords to each statement, which were then reviewed and verified by the other researcher. In cases where differences of opinion arose, discussions took place, leading to a consensus on keyword allocation. Subsequently, all statements were checked for duplicates within each keyword, and those duplications were eliminated. This process resulted in a final list of 84 statements eligible for sorting and rating.

*Perspective Chapter: Online Learning in Professional and Vocational Education – Seeking… DOI: http://dx.doi.org/10.5772/intechopen.112513*

#### **4.2 Clustering the statements**

**Figure 1** shows the point map of the sorting of the 84 statements. Statements the participants more often placed together in a pile are closer to each other.

The data analysis of the sorting step involved multi-dimensional scaling (MDS). This technique places each statement as a unique 'point' on a map. This point map represents each statement as a separate point on a two-dimensional (X, Y) space. MDS produces a statistic called 'stress index' (with values ranging from 0 to 1) to indicate the extent to which the concept map reflects the raw sorting as represented by a binary similarity matrix. The stress value for the MDS solution (goodness of fit) was 27, which is fairly acceptable in terms of reliability of the results [28].

Next, a hierarchical cluster analysis was performed to support the decision regarding an optimal number of clusters of statements. The software provides suggestions for different cluster solutions. In general, there is neither a right nor a wrong number of clusters but rather a solution that the researchers interpreted as logically and sensible. The optimal number of clusters depends on the coherence of statements belonging to a particular cluster. This can be judged by scores on the 'bridging factor' (values ranging from 0 to 1). The value of the bridging factor represents the extent to which statements within the cluster are sorted with nearby statements. A lower bridging value means a statement within the cluster is more often sorted with nearby statements, while a higher bridging value indicates the statement is sorted with statements that are further away [20].

This analysis resulted into seven clusters with acceptable bridging values (BF): Didactics (BF = 0.09), Use of technology (BF = 0.18), Planning and scheduling (BF = 0.26), Efficiency (BF = 0.25), Involvement (BF = 0.20), Interaction (BF = 0.71) and Preconditions (BF = 0.36). The software suggested terms (labels) for the identified clusters and the researchers altered the labels slightly to make them more understandable for a varied audience (**Figure 2**).

#### **4.3 Rating the statements**

All students and teachers were invited to rate the 84 statements again, first in terms of their importance (278 responses) and then on feasibility (196 responses). These statements were rated on a five-point scale ranging from 1 (low) to 5 (high). For the entire group of respondents and the entire group of 84 statements, the mean

**Figure 1.** *Point map of the 84 statements.*

**Figure 2.** *The final cluster map of 7 clusters and their labels.*

score was 3.74 for importance and 3.58 for feasibility, respectively. Inspection of the scores for both sub-groups revealed that teachers and students did not differ significantly. The mean scores for the entire group of respondents of the clusters are displayed in **Table 2**.

The number of statements per cluster varied between 6 (planning and scheduling) and 18 (didactics and preconditions). The mean scores indicated that the clusters were considered to be rather or slightly important. The mean scores ranged from 3.44 to 3.96. This also applies to the mean scores for feasibility, which ranged from 3.33 to 3.88.

Below are brief descriptions of the statements in each cluster.

**Didactics** had quite comparable scores on importance and feasibility. In total, 18 statements were grouped in this cluster. They expressed ideas about the need to encourage active learning activities during online meetings and the recording and distribution of online lectures.

**Use of technology** had the highest score for feasibility and a slightly lower score for importance. This indicates the respondents expect a rather smooth implementation of the ideas in this cluster. The eight statements address the need to improve the use of the communication environment (MS Teams) and the learning environment (Moodle).

**Planning and scheduling** had a high score for importance and an almost equally high score for feasibility. This indicates that implementation is regarded as fairly


#### **Table 2.**

*Mean scores for importance and feasibility per cluster\*.*

#### *Perspective Chapter: Online Learning in Professional and Vocational Education – Seeking… DOI: http://dx.doi.org/10.5772/intechopen.112513*

promising. This cluster consists of six statements that generally refer to the additional consequences of organising, planning and scheduling online activities.

Compared to the other clusters, *involvement* had a somewhat lower score for importance and the lowest score for feasibility. This indicates that implementing the ideas in this cluster may involve some difficulties. The seven statements in this cluster refer to ideas for encouraging students' commitment to online education. Examples are the needs to increase small group activities and combine online and face-to-face activities in a well-thought-out manner.

**Efficiency** received the lowest score for importance but a somewhat higher score for feasibility. The 17 statements in this cluster refer to the advantages of online education (e.g. saving time and expenses related to travelling to campus, increased opportunities to invite foreign guest speakers for lectures).

**Interaction** had a high score for importance and a lower score for feasibility. That indicates that implementing the ideas in this cluster may require substantial effort. The 10 statements in this cluster all refer to ideas that underpin the necessity of frequent interaction between students themselves and between students and teaching staff. The ideas about interaction did not necessarily express a desire for online interaction; they also referred to a desire for scheduling conventional face-to-face meetings.

**Preconditions** had the highest score for importance combined with a lower score for feasibility. This may imply that implementing the ideas was rated as somewhat less promising. This cluster consisted of 18 statements about ideas related to creating clarity about how to behave in online settings, uniformity in rules and procedures, and the need to support students and teachers in increasing their digital competencies.

#### **4.4 Prioritising statements for policy purposes**

To support the further development of an online learning policy, a Go Zone plot was created. It shows the relationship between the importance and feasibility per statement [29]. The division into quadrants is based on the mean scores for importance (3.74) and feasibility (3.58). This illustrates which statements the respondents considered most important and most feasible. The top right quadrant of **Figure 3** also displays the statements considered most important and most feasible.

Since there was no significant difference in the scores students and teachers assigned for importance and feasibility, the Go Zone plot for the entire group of respondents is displayed in **Figure 3**.

In total, 29 statements are grouped into the top right quadrant of **Figure 3**, meaning the respondents (teachers and students) considered these 29 ideas to be the most

#### **Figure 3.**

*Go zone plot of the relationship between importance and feasibility for the entire group of respondents.*


#### **Table 3.**

*Examples of the most important and feasible statements per cluster (total 29 statements).*

important and most feasible. The 29 statements were not equally distributed across the seven clusters. For example, none of the statements from the efficiency cluster were present in these 29 statements. About a third of the 29 statements belonged to the didactics cluster, and seven were in the preconditions cluster. **Table 3** shows the division of statements across clusters and provides examples of statements.
