10 Sequence Analysis in Education: Principles, Technique, and Tutorial with R
1 Introduction
Patterns exist everywhere in our life, from the sequence of genes to the order of steps in cooking recipes. Discovering patterns, variations, regularities, or irregularities is at the heart of scientific inquiry and, therefore, several data mining methods have been developed to understand patterns. Sequence analysis —or sequence mining— was developed almost four decades ago to address the increasing needs for pattern mining [1]. Ever since, a wealth of applications, algorithms, and statistical tools have been developed, adapted, or incorporated into the array of sequence analysis. Since sequence mining has been conceptualized, it has grown in scale of adoption and range of applications across life and social sciences [5] and education research was no exception (e.g., [7]). As a data mining technique, sequence mining has been commonly implemented to identify hidden patterns that would otherwise be missed using other analytical techniques and find interesting subsequences (parts of the sequence) that have practical significance or unexpected sequences that we did not know existed [9]. For instance, by mining sequences of collaborative dialogue, we could identify which sequences are followed by more conductive to more argumentative interactions, and what sequences are crucial to the collaborative process. A literature review of the common applications follows in the next section.
Learning is a process that unfolds in time, a process that occurs in sequences of actions, in repeated steps, in patterns that have meanings and value for understanding learners’ behavior [10]. The conceptualization of learning as a process entails two important criteria: process as a sequence of states that unfold in time and process as a transformative mechanism that drives the change from one state to another [11]. Thereupon, methods such as sequence mining have gained increasing grounds and amassed a widening repertoire of techniques in the field of education to study the learning process. In particular, sequence mining has been used to harness the temporal unfolding of learners’ behavior using digital data, transcripts of conversations, or behavioral states [12]. Nevertheless, sequence mining can be used to study non-temporal sequences such as protein sequences and other types of categorical data [4].
What makes sequences in education interesting is that they have patterns of repeated or recurrent sequences. Finding such patterns has helped typify learners’ behaviors and identify which patterns are associated with learning and which are associated with unfavorable outcomes [7]. Sequence mining can also describe a pathway or a trajectory of events, for example, how a student proceeds from enrolment to graduation [14], and help to identify the students who have a stable trajectory, who are turbulent, and who are likely to falter along their education [7].
2 Review of the literature
In recent years, sequence analysis has become a central method in learning analytics research due to its potential to summarize and visually represent large amounts of student-related data. In this section we provide an overview of some of the most representative studies in the published literature. A summary of the studies reviewed in this section can be seen in Table 10.1. A common application of sequence analysis is the study of students’ log data extracted from their interactions with online learning technologies (mosty learning management systems, LMSs) throughout a learning session [15–17]. In some studies, the session is well-delimited, such as the duration of a game [18] or moving window [19], but in most cases it is inferred from the data, considering a session as an uninterrupted sequence of events [15, 16]. Few are the studies in which longer sequences are studied, covering a whole course or even a whole study program [7, 14, 20]. In such studies, the sequences are not composed of instantaneous interactions but rather of states that aggregate students’ information over a certain period, for example, we can study students’ engagement [14], learning strategies [7], or collaboration roles [20] for each course in a study program.
Most of the existing research has used clustering techniques to identify distinct groups of similar sequences. Agglomerative Hierarchical Clustering (AHC) has been the most recurrently used technique, with a wealth of distance measures such as Euclidean [7, 15], Longest Common Subsequence [18], Longest Common Prefix [21], and Optimal Matching [16]. Other works have relied on Markovian Models [13, 20] or differential sequence mining [6]. Throughout the remainder of the book, we provide an introduction to sequence analysis as a method, describing in detail the most relevant concepts for its application to educational data. We provide a step-by-step tutorial of how to implement sequence analysis in a data set of student log data using the R programming language.
| Ref. | Context | Time scheme | Actor | Alphabet | Clustering algorithm |
|---|---|---|---|---|---|
| [6] | 40 students | Learning activity (5 days) | Student | LMS events | Differential sequence mining (core algorithm) |
| [19] | 1 middle school class (40 students) | Learning activity (5 days) | Student | LMS events (e.g., Read, Linkadd) | Differential sequence mining (core algorithm, SPAMc) |
| [16] | 1 university course (290 students) | Session | Student-session | LMS events | AHC (Optimal matching) |
| Course | Student-course | Tactics obtained from previous clustering | AHC (Euclidean distance) | ||
| [15] | 3 courses: one university course with 3 course offerings (1135 students), another university course with 2 course offerings (487 students), and a MOOC with a single offering (368 students) | Session | Student-session | LMS events (e.g., content_access, download) | First Order Markov Model |
| Course | Student-course | Tactics obtained from previous clustering | AHC (Euclidean distance) | ||
| [14] | 15 university courses (106 students) | Study program (15 courses) | Student | Engagement state (e.g., Active, Average) | Hidden Markov Models |
| [18] | 1 educational escape room game in a university course (96 students) | Escape room game (1h 45m) | Team | Game activity (e.g., hint obtained, puzzle solving) | AHC (Longest Common Subsequence) |
| [20] | 10 university courses (329 students) | Study program (10 courses) | Student | Roles in the group (e.g., Leaders, Mediators) | Mixture Hidden Markov Models |
| [7] | 10 university courses (135 students) | Session | Student-session | LMS event (e.g., Course main view, Forum consume) | Mixture Hidden Markov Models |
| Course | Student-course | Tactics obtained from previous clustering (e.g., Lecture read, Forum read) | AHC (Euclidean distance) | ||
| Study program (10 courses) | Student | Course-level strategies from previous clustering (e.g., Light interactive, Moderate interactive) | AHC (Euclidean distance) | ||
| [21] | 1 university courses, 4 Course offerings (200 students) | Week | Group of students | Interaction type on forum (e.g., Discuss, Argue) | |
| Session | Student-session | Interaction type on forum (e.g., Discuss, Argue) | AHC (Longest Common Prefix) |
3 Basics of sequences
Sequences are ordered lists of discrete elements (i.e., events, states or categories). Such elements are discrete (in contrast to numerical values such as grades) and are commonly organized chronologically. Examples include sequence of activities, sequence of learning strategies, or sequence of behavioral states [23]. A sequence of learning activities may include (play video - solve exercise - taking quiz - access instructions) [13], other examples include sequence of game moves e.g., (solve puzzle - request hint - complete game) [18], or collaborative roles, for instance, (leader - mediator - isolate) [20].
Before going into sequence analysis, let’s discuss a basic example of a sequence inspired by [14]. Let’s assume we are tracking the engagement states of students from a course to the next and for a full year that has five courses. The engagement states can be either engaged (when the student is fully engaged in their learning), average (when the student is moderately engaged), and disengaged (when the student is barely engaged). Representing the sequence of engagement states of two hypothetical students may look like the example on Table 10.2.
| Actor | Course 1 | Course 2 | Course 3 | Course 4 | Course 5 |
|---|---|---|---|---|---|
| Student 1 | Average | Engaged | Engaged | Engaged | Engaged |
| Student 2 | Average | Disengaged | Disengaged | Disengaged | Disengaged |
The first student starts in course 1 with an Average engagement state, in Course 2, the student is engaged, and so in all the subsequent courses Course 3, Course 4, and Course 5. The student in row 2 has a Disengaged state in course 2 onwards. As we can see from the two sequences here, there is a pattern that repeats in both sequences (both students stay 4 consecutive courses in the same state). In real-life examples, sequences are typically longer and in larger numbers. For instance, the paper by [14] contains 106 students for a sequence of 15 courses. Finding repeated patterns of engaged states similar to the first student or repeated patterns of disengaged states like the other student would be interesting and helpful to understand how certain subgroups of students proceed in their education and how that relates to their performance.
3.1 Steps of sequence analysis
Several protocols exist for sequence analysis that vary by discipline, research questions, type of data, and software used. In education, sequence analysis protocol usually follows steps that include preparing the data, finding patterns, and relating these patterns to other variables e.g., performance e.g., [16]. The protocol which will be followed in this manual includes six steps:
Identifying (or coding) the elements of the sequence, commonly referred to as alphabet
Specifying the time window or epoch (time scheme) or sequence alignment scheme
Defining the actor and building the sequence object
Visualization and descriptive analysis
Finding similar groups or clusters of sequences,
Analyzing the groups and/or using them in subsequent analyses.
3.1.1 The alphabet
The first step of sequence analysis is defining the alphabet which are the elements or the possible states of the sequence [23]. This process usually entails “recoding” the states to optimize the granularity of the alphabet. In other words, to balance parsimony versus granularity and detail of the data. Some logs are overly detailed and therefore would require a careful recoding by the researcher [4]. For instance, the logs of Moodle (the LMS) include the following log entries for recoding students’ access to the quiz module: quiz_attempt, quiz_continue_attempt, quiz_close_attempt, quiz_view, quiz_view_all, quiz_preview. It makes sense here to aggregate (quiz_attempt, quiz_continue_attempt, quiz_close_attempt) into one category with the label attempt_quiz and (quiz_view, quiz_view all, quiz preview) to a new category with the label view_quiz. Optimizing the alphabet into a reasonable number of states also helps reduce complexity and facilitates interpretation. Of course, caution should be exercised not to aggregate meaningfully distinct states to avoid masking important patterns within the dataset.
3.1.2 Specifying the time scheme
The second step is to define a time scheme, time epoch or window for the analysis. Sometimes the time window is fairly obvious, for instance, in case a researcher wants to study students’ sequence of courses in a program, the window can be the whole program e.g., [14]. Yet, oftentimes, a decision has to be taken about the time window which might affect the interpretation of the resulting sequences. For example, when a researcher is analyzing the sequence of interactions in a collaborative task, he/she may consider the whole collaborative task as a time window or may opt to choose segments or steps within the task as time epochs. In the same way, analyzing the sequence of tasks in a course, one would consider the whole course to be the time window for analysis or analyze the sequence of steps in each course task e-g., [24].
In online learning, the session has been commonly considered the time window e.g., [13, 16]. A session is an uninterrupted sequence of online activity which can be inferred from identifying the periods of inactivity as depicted in Figure 10.1. As can be seen, a user can have multiple sessions across the course. There is no standard guideline for what time window a researcher should consider, however, it is mostly defined by the research questions and the aims of analysis.
3.1.3 Defining the actor
The third important step is to define the actor or the unit of analysis of the sequences (see the actor in Table 10.3 or User in Table 10.4). The actor varies according to the type of analysis. When analyzing students’ sequences of actions, we may choose the student to be the actor and build a sequence of all student actions e.g., [24]. In online learning, sequence mining has always been created for “user sessions” i.e., each user session is represented as a sequence e.g., [13, 25] and therefore, a user typically has several sessions along the course. In other instances, you may be interested in the study of the sequences of the students’ states, for example engagement states in [14] where the student was the actor, or a group of collaborating students’ interactions as a whole such as [21] where the whole group is the actor. In the review of the literature, we have seen examples of such decisions and how varied they can be.
3.1.4 Building the sequences
This step is specific to the software used. For example, in TraMineR the step includes specifying the dataset on which the building of the sequences is based and telling TraMineR the alphabet, the time scheme, and the actor id variable, as well as other parameters of the sequence object. This step will be discussed in detail in the analysis section.
3.1.5 Visualizing and exploring the sequence data
The fourth step is to visualize the data and perform some descriptive analysis. Visualization allows us to summarize data easily and to see the full dataset at once. TraMineR includes several functions to plot the common visualization techniques, each one showing a different perspective.
3.1.6 Calculating the dissimilarities between sequences
The fifth step is calculating dissimilarities or distances between pairs of sequences. Dissimilarity measures are a quantitative estimation of how different —or similar— the sequences are. Since there are diverse contexts, analysis objectives and sequence types, it is natural that there are several methods to compute the dissimilarities based on different considerations.
Optimal matching (OM) may be the most commonly used dissimilarity measure used in social sciences and possibly also in education [8]. Optimal matching represents what it takes to convert or edit a sequence to become identical to another sequence. These edits may involve insertion, deletion (together often called indel operations) or substitution. For instance, in an example in Table 10.3, where we see a sequence of five students’ engagement states, we can edit Vera’s sequence and substitute the disengaged state with an average state; Vera’s sequence will become identical with Luis’ sequence. That is, editing Vera’s sequence takes one substitution to convert her sequence to that of Luis. We can also see that it will take four substitutions to convert Anna’s sequence to Maria’s sequence. In other words, Anna’s sequence is highly dissimilar to Maria. Different types of substitutions can be given different costs depending on how (dis)similar the two states are viewed (referred to as substitution costs). For example, the cost of substituting state engaged with state average might have a lower cost than substituting engaged with disengaged, since being disengaged is regarded most dissimilar to being engaged while average engagement is more similar to it. Since contexts differ, there are different ways of defining or computing the pairwise substitution costs matrix.
Optimal matching derives from bioinformatics where transformations such as indels and substitutions are based on actual biological processes such as the evolution of DNA sequences. In many other fields such a transformation process would be unrealistic. In social sciences, [8] outlined five socially meaningful aspects and compared dissimilarity measures to determine how sensitive they are to the different aspects. These similarities are particularly relevant since learning, behavior, and several related processes e.g., progress in school or transition to the labor market are essentially social processes. We explain these aspects based on an example using fictional data in Table 10.3 following [14].
| Actor | Course 1 | Course 2 | Course 3 | Course 4 | Course 5 |
|---|---|---|---|---|---|
| Maria | Engaged | Engaged | Engaged | Engaged | Average |
| Vera | Disengaged | Disengaged | Average | Engaged | Engaged |
| Anna | Average | Disengaged | Disengaged | Average | Average |
| Luis | Disengaged | Average | Average | Engaged | Engaged |
| Bob | Engaged | Engaged | Average | Engaged | Engaged |
Experienced states: how similar are the unique states forming the sequence. For instance, Maria and Bob in Table 10.3 have both experienced the same states (engaged and average).
Distribution of the states: how similar is the distribution of states. We can consider that two sequences are similar when students spend most of their time in the same states. For instance, Bob and Maria have 80% engaged states and 20% average states.
Timing: the time when each state occurs. For instance, two sequences can be similar when they have the same states occurring at the same time. For instance, Vera and Luis start similarly in a disengaged state, visit the average state in the middle, and finish in the engaged state.
Duration: the durations of time spent continuously in a specific state (called spells) e.g., the durations of engaged states shared by the two sequences. For instance, Vera and Anna both had spells of two successive states in the disengaged state while Bob had two separate spells in the engaged state (both of length 2).
Sequencing: The order of different states in the sequence, for instance, Vera and Luis had similar sequences starting as disengaged, moving to average and then finishing as engaged.
Of the aforementioned aspects, the first two can be directly determined from the last three. Different dissimilarity measures are sensitive to different aspects, and it is up to the researcher to decide which aspects are important in their specific context. Dissimilarity measures can be broadly classified in three categories [8]:
distance between distributions,
counting common attributes between sequences, and
edit distances.
Category 1 includes measures focusing on the distance between distributions including, e.g., Euclidean distance and \(\chi^2\)-distance that compare the total time spent in each state within each sequence. The former is based on absolute differences in the proportions while the latter is based on weighted squared differences.
Category 2 includes measures based on counting common attributes. For example, Hamming distances are based on counting the (possibly weighted) sum of position wise mismatches between the two sequences, the length of the longest common subsequence (LCS) is the number of shared states between two sequences that occur in the same order in both, while the subsequence vector representation -based metric (SVRspell) is counted as the weighted number of matching subsequences.
Category 3 includes edit distances that measure the costs of transforming one sequence to another by using edit operations (indels and substitutions). They include (classic) OM with different cost specifications as well as variants of OM such as OM between sequences of spells (OMspell) and OM between sequences of transitions (OMstran).
Studer and Ritschard [8] give recommendations on the choice of dissimilarity measure based on simulations on data with different aspects. If the interest is on distributions of states within sequences, Euclidean and \(\chi^2\)-distance are good choices. When timing is of importance, the Hamming distances are the most sensitive to differences in timing. With specific definitions also the Euclidean and \(\chi^2\)-distance can be made sensitive to timing – the latter is recommended if differences in rare events are of particular importance. When durations are of importance, then OMspell is a good choice, and also LCS and classic OM are reasonable choices. When the main interest is in sequencing, good choices include OMstran, OMspell, and SVRspell with particular specifications. If the interest is in more than one aspect, the choice of the dissimilarity measure becomes more complex. By altering the specifications in measures such as OMstran, OMspell, and SVRspell the researcher could find a balance between the desired attributes. See [8] for more detailed information on the choice of dissimilarity measures and their specifications.
Dissimilarities are hard to interpret as such (unless the data are very small), so further analyses are needed to decrease the complexity. The most typical choice is to use cluster analysis for finding groups of individuals with similar patterns [22]. Other distance —or dissimilarity—based techniques include visualizations with multidimensional scaling [27], finding representative sequences [28], and ANOVA-type analysis of discrepancies [29].
3.1.7 Finding similar groups or clusters of sequences
The sixth step is finding similar sequences, i.e., groups or patterns within the sequences where sequences within each group or cluster are as close to each other as possible and as different from other patterns in other clusters as possible. For instance, we can detect similar groups of sequences that show access patterns to online learning which are commonly referred to as tactics e.g., [7]. Such a step is typically performed using a clustering algorithm which may –or may not– require dissimilarity measures as an input [22, 26]. Common clustering algorithms that use a dissimilarity matrix are the hierarchical clustering algorithms. Hidden Markov models are among the most non-distance based cluster algorithms. See the remaining chapters about sequence analysis for examples of these algorithms [30–32].
3.1.8 Analyzing the groups and/or using them in subsequent analyses
Analysis of the identified patterns or subgroups of sequences is an important research question in many studies and oftentimes, it is the guiding research question. For instance, researchers may use log data to create sequences of learning actions, identify subgroups of sequences, and examine the association between the identified patterns and performance e.g., [6, 7, 13], associate the identified patterns with course and course delivery [15], examine how sequences are related to dropout using survival analysis [14], or compare sequence patterns to frequencies [21].
3.2 Introduction to the technique
Before performing the actual analysis with R code, we need to understand how the data is processed for analysis. Four important steps that require more in-depth explanation will be clarified here, those are: defining the alphabet, the timing scheme, specifying the actor, and visualization. Oftentimes, the required information to perform the aforementioned steps are not readily obvious in the data and therefore some preparatory steps need to be taken to process the file.
The example shown in Table 10.4 uses fictional log trace data similar to those that come from LMSs. To build a sequence from the data in Table 10.4, we can use the Action column as an alphabet. If our aim here is to model the sequence of students’ online actions, this is a straightforward choice that requires no preparation. Since the log trace data has no obvious timing scheme, we can use the session as a time scheme. To compute the session, we need to group the actions that occur together without a significant delay between actions (i.e., lag) that can be considered as an inactivity (see Section 10.3.1.2). For instance, Layla’s actions in Table 10.4 started at 18:44 and ended at 18:51. As such, all Layla’s actions occurred within 7 minutes. As Table 10.4 also shows, the first group of Layla’s actions occur within 1 to 2 minutes of lag. The next group of actions by Layla occur after almost one day, an hour and six minutes (1506 minutes) which constitutes a period of inactivity long enough to divide Layla’s actions into two separate sessions. Layla’s actions on the first day can be labeled Layla-session1 and her actions on the second day are Layla-session2. The actor in this example is a composite of the student (e.g., Layla) and the session number. The same for Sophia and Carmen: their actions occurred within a few minutes and can be grouped into the sessions. Given that we have the alphabet (Action), the timing scheme (session), and the actor (user-session), the next step is to order the alphabet chronologically. In Table 10.4, the actions were sequentially ordered for every actor according to their chronological order. The method that we will use in our guide requires the data to be in so-called “wide format”. This is performed by pivoting the data, or creating a wide form where the column names are the order and the value of the Action column is sequentially and horizontally listed as shown in Table 10.5.
| User | Action | Time | Lag | Session | Order |
|---|---|---|---|---|---|
| Layla | Calendar | 9.1.2023 18:44 | - | Layla session 1 | 1 |
| Layla | Lecture | 9.1.2023 18:45 | 1 | Layla session 1 | 2 |
| Layla | Instructions | 9.1.2023 18:47 | 2 | Layla session 1 | 3 |
| Layla | Assignment | 9.1.2023 18:49 | 2 | Layla session 1 | 4 |
| Layla | Lecture | 9.1.2023 18:50 | 1 | Layla session 1 | 5 |
| Layla | Video | 9.1.2023 18:51 | 1 | Layla session 1 | 6 |
| Sophia | Lecture | 9.1.2023 20:08 | - | Sophia session 1 | 1 |
| Sophia | Instructions | 9.1.2023 20:12 | 4 | Sophia session 1 | 2 |
| Sophia | Assignment | 9.1.2023 20:14 | 2 | Sophia session 1 | 3 |
| Sophia | Assignment | 9.1.2023 20:18 | 4 | Sophia session 1 | 4 |
| Sophia | Assignment | 9.1.2023 20:21 | 3 | Sophia session 1 | 5 |
| Carmen | Lecture | 10.1.2023 10:08 | - | Carmen session 1 | 1 |
| Carmen | Video | 10.1.2023 10:11 | 3 | Carmen session 1 | 2 |
| Layla | Instructions | 10.1.2023 19:57 | 1506 | Layla session 2 | 1 |
| Layla | Video | 10.1.2023 20:01 | 4 | Layla session 2 | 2 |
| Layla | Lecture | 10.1.2023 20:08 | 7 | Layla session 2 | 3 |
| Layla | Assignment | 10.1.2023 20:14 | 6 | Layla session 2 | 4 |
| Actor | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Layla session1 | Calendar | Lecture | Instructions | Assignment | Lecture | Video |
| Sophia session1 | Lecture | Instructions | Assignment | Assignment | Assignment | |
| Carmen session1 | Lecture | Video | ||||
| Layla session2 | Instructions | Video | Lecture | Assignment |
The following steps are creating a sequence object using sequence mining software and using the created sequence in analysis. In our case, we use the TraMineR framework which has a large set of visualization and statistical functions. Sequences created with TraMineR also work with a large array of advanced tools, R packages, and extensions. However, it is important to understand sequence visualizations before delving into the coding part.
3.3 Sequence Visualization
Two basic plots are important here and therefore will be explained in detail. The first is the index plot (Figure 10.2) which shows the sequences of stacked colored bars representing spells, with each token represented by a different color. For instance, if we take Layla’s actions (in session1) and represent them as an index plot, they will appear as shown in Figure 10.2 (see the arrow). Where the Calendar is represented as a purple bar, the Lecture as a yellow bar, and instructions as an orange bar etc. Figure 10.2 also shows the visualization of sequences in Table 10.5 and you can see each of the session sequences as stacked colored bars following their order in the table. Nevertheless, sequence plots commonly include a large number of sequences that are of the order of hundreds or thousands of sequences and may be harder to read than the one presented in the example (see examples in the next sections).
The distribution plot is another related type of sequence visualization. Distribution plots —as the name implies— represent the distribution of each alphabet at each time point. For example, if we look at Figure 10.3 (top) we see 15 sequences in the index plot. At time point 1, we can count eight Calendar actions, two Video actions, two Lecture actions and one Instruction action. If we compute the proportions: we get 8/15 (0.53) of Calendar actions; for Video, Assignment, and Lecture we get 2/15 (0.13) in each case, and finally Instructions actions account for 1/15 (0.067). Figure 10.3 (bottom) shows these proportions. At time point 1, we see the first block Assignment with 0.13 of the height of the bar, followed by the Calendar which occupies 0.53, then a small block (0.067) for the Instructions, and finally two equal blocks (0.13) representing the Video and Lecture actions.
Since the distribution plot computes the proportions of activities at each time point, we see different proportions at each time point. Take for example, time point 6, we have only two actions (Video and Assignment) and therefore, the plot has 50% for each action. At the last point 7, we see 100% for Lecture. Distribution plots need to be interpreted with caution and in particular, the number of actions at each time point need to be taken into account. One cannot say that at the 7th time point, 100% of actions were Lecture, since it was the only action at this time point. Furthermore, distribution plots do not show the transitions between sequences and should not be interpreted in the same way as the index plot.
4 Analysis of the data with sequence mining in R
4.1 Important packages
The most important package and the central framework that we will use in our analysis is the TraMineR package. TraMineR is a toolbox for creating, describing, visualizing and analyzing sequence data. TraMineR accepts several sequence formats, converts to a large number of sequence formats, and works with other categorical data. TraMineR computes a large number of dissimilarity measures and has several integrated statistical functions. TraMineR has been mainly used to analyze live event data such as employment states, sequence of marital states, or other life events. With the emergence of learning analytics and educational data mining, TraMineR has been extended into the educational field [33]. In the current analysis we will also need the packages TraMineRextras, WeightedCluster, and seqhandbook, which provide extra functions and statistical tools. The first code block loads these packages. In case you have not already installed them, you may need to install them.
library(TraMineR)
library(TraMineRextras)
library(WeightedCluster)
library(seqhandbook)
library(tidyverse)
library(rio)
library(cluster)
library(MetBrewer)
library(reshape2)4.2 Reading the data
The example that will be used here is a Moodle log dataset that includes three important fields: the User ID (user), the time stamp (timecreated), and the actions (Event.context). Yet, as we mentioned before, there are some steps that need to be performed to prepare the data for analysis. First, the Event.context is very granular (80 different categories) and needs to be re-coded as mentioned in the basics of sequence mining section. We have already prepared the file with a simpler coding scheme where, for example, all actions intended as instructions were coded as instruction, all group forums were coded as group_work, and all assignment work was coded as Assignment. Thus, we have a field that we can use as the alphabet titled action. The following code reads the original coded dataset.
Seqdatas <-
import("https://github.com/lamethods/data/raw/main/1_moodleLAcourse/Events.xlsx")