Blended learning (BL), also known and as hybrid learning, is a way of teaching that combines traditional face to face classroom methods (with technology mediated) and on-line educational material. This allows students to have access to teaching material, even after the lesson is finished and provides them with a more personalized learning environment. Blended learning differs from other on-line methods in the aspect of counting on “face to face” teaching methods as well [
1]. It also provides a shift from traditional teaching to a more interactive one, where teachers act more as guides and supervisors, establishing a more personal relation with their students, than simply act as the ones who deliver knowledge to a large audience. Learning on the other side becomes more interactive than passive, as students become more interactive together and with their teacher [
1]. Blended learning classes can statistically produce better results than their face-to-face, non-hybrid equivalents. This is probably happening since teachers shift their role to managers and facilitators and because students’ learning experiences can be expanded [
1].
Face-to-face driver allows either students who are struggling or working above the average educational level to progress at their own pace using technology in the classroom. It is the closest model to a typical school structure.
This model is more common in primary schools, where students switch between different stations on a fixed schedule–either spending face-to-face time with their instructor or working online.
In the Flex model, students learn and practice autonomously in a digital environment. Teachers are available in the classroom to provide on-site support and help if it is needed.
With this approach, students learn exclusively online, but they can complete their coursework to a computer lab, where adults, who are not trained teachers can act as supervisors. This works well with differentiated learning and allows schools to offer courses for which they have no teacher or not enough teachers.
The self-blend model is more popular in high schools and allows mostly those students who are highly self-motivated to take classes beyond what is already offered at their traditional school environment and supplement their learning through online courses offered remotely.
This approach of BL is becoming increasingly popular, by about 15% each year, indicating the number of students participating in online driver programs, which provides them more flexibility and independence in their daily schedules. This means that even though students can interact with their teachers online if it is needed, they mainly work remotely on material which is primarily delivered via an online learning platform.
Comes to no surprise that education has been transformed the recent decades by the rapid spread of technology. Higher institutions, as the leading force in delivering educational innovations, are trying to adopt to modern society’s educational needs. Therefore, Blended Learning can be used in a wide range of academic disciplines through a variety of pedagogical approaches and models.
1.1. Literature Review
In terms of the students’ attitude towards blended courses, one study [
3] has indicated that in general, students show positive attitude towards blended courses and that factors affiliated with learning climate; perceived enjoyment; perceived usefulness; system functionality; social interaction; content feature and performance expectation are also significantly related to students’ satisfaction in blended courses. Another study [
4] has proved that a lot of students who attend blended courses not only show positive attitude towards these courses but they also achieve better results in the context of their performance in comparison to students enrolled in conventional courses. That finding is also in line with the studies [
5,
6,
7,
8,
9,
10] which have pointed out that blended learning has positive impact on students’ performance.
In the context of students’ performance in blended courses, one study [
11] has considered the following four main categories as especially important and significant factors that have great effect on students’ academic performance (SAP):
The interaction processes refer to the interaction and communication between students and their instructors through the use of Internet. Such interaction aims at improving the quality of learning by providing students with access to resources and services. However, the objective of that interaction is not encircled on replacing the traditional classroom setting. A great extent of interaction is achieved by discussion fora and the message delivery system. Through fora, students can move on interchanging valuable information on syllabus, asking questions and take advantage of all the benefits of the peer-to peer learning. Thereby, students can gain knowledge on syllabus in terms of a collaborative learning practice. The message delivery system which is mainly a part of a Learning Management System (LMS), allows students to send messages to educators in order to ask them questions and to ask them for help. Thus, educators have the golden opportunity to provide students with extra feedback.
The characteristics of the students refer to the social and psychological characteristics that could have a positive or negative effect on students’ performance. These features could vary between intrinsic students’ characteristics and characteristics which have affinity with students’ background. The intrinsic students’ characteristics is a set of features which includes students’ educational background, students’ social status, demographic characteristics, students’ level of internal self-motivation, students attitude towards a course and students’ learning preferences. Nevertheless, other characteristics are related to the effect of students’ external environment on their performance. The students’ encouragement by their family and the way students are urged by educators are factors which could affect students’ final achievement.
It is important to stress on the fact that students participating in a course, constitute a specific class. The class is bristled with all the students’ characteristics which have previously been referred. On the ground that the class is bristled with students’ features which have negative effect on their performance (negative features), the class will encounter learning difficulties and will face significant learning challenges. In that way, the students’ negative intrinsic or external features are translated into negative class features. On the other hand, when a class is bristled with students’ characteristics which have positive effect on their performance (positive features), the class will thrive. Thereby, it is crucial for an educator to meet the learning challenges in his/her class. The educators could more easily meet the learning challenges in their classes in blended courses in contradiction to the fully online courses. That holds true on the ground that face-to face approach which is mainly deployed in blended courses offers educators a great opportunity to work on students’ negative features and come up with the necessary remedial action. In terms of a fully online course (not face-to face approach), educators could only work on students’ negative features through e-mails and scheduled live video lectures (video-conferences) That is a main reason why a majority of Institutions prefer the blended courses to the fully online courses.
Another study [
12] has attempted to shed more light on the relationships concerning motivations; emotions, cognitive; meta-cognitive and learning strategies and their impact on learning performance in blended courses. Their results suggested that negative emotions play a meaningful role between expectancy (a component of motivation) and learning strategies and that the expectancy component of motivation positively influences meta-cognitive strategies. That work is in line with the findings referred in the study [
11], insinuating that students’ self motivation which is included in the set of students’ intrinsic characteristics has significant positive effect on students’ final achievement in blended courses. Though, it is important to set out in the difficulty of measuring factors in regard to students’ psychological characteristics. In other words, the students’ emotional engagement could not easily be measured. On that account, a lot of studies stress on students’ behavioral engagement which can be measured through the students’ interaction with the learning process reflected on students’ interaction with resources and learning activities. The effect of students’ behavioral engagement on the students’ final achievement will be explained in the next sections.
The issue of students’ performance in blended courses has also been addressed in another work [
13]. This study has clarified that students’ performance in blended courses is affected by the success of two systems, the technical system, and the social system. In a more elaborate detail, the technical system is reflected on the role of e-learning and the social system is reflected on the motivation and learning climate. Thereby, that study has shown that the e-learning part could play a significant role in the generic success of blended courses and that a well-designed technical system contributes to a better learning outcome.
In parallel manner, another research [
14] proved that high achievers in a blended course were students who had fully participated into online activities. Therefore, students’ engagement explained by their participation in the online activities could be deemed to be a significant factor which contributes to high achievement in blended courses. Another work [
15] focuses on the Moodle usage practices and their impact on students’ performance in the context of a specific blended course proving that the Moodle system usage has accounted for the 20.2% of variance in the students’ final grade insinuating that students’ engagement reflecting on the e-learning environment system usage, is an important factor which significantly affects the students’ performance in blended courses. It is also important to refer to another study [
16] which has indicated that students’ achievement in blended courses bears on their self-efficacy and e-learning motivation highlighting the important role of e-learning part. This study is also in line with the studies [
5,
7,
8,
9,
10] which have also proved that the students’ achievement in blended courses is dependent on students’ attitude towards e-learning system usage.
It is also vital to refer to a study [
17] which made use of a binary logistics regression analysis to predict students’ performance in two blended business courses. The binary logistics’ regression outcome on a data set including social; individual and academic factors indicated that students’ self-regulation; skills and learning presence in the community are strong predictors of students’ final achievement. Needless to say, that the academic factors were reflected by students’ engagement data including attendance; credit assignments; first quiz grades and semester grades.
In the area of students at risk, the research interest is directed into developing a warning system for students at risk. A lot of studies [
18,
19,
20,
21,
22,
23,
24,
25] have pointed out that engagement data could be analyzed in order to identify students at risk. Some of these studies [
18,
19] have analyzed Moodle LMS engagement data with a view to developing an early warning system for students at risk. These studies have stressed on students’ behavioral engagement data as strong predictors of students’ performance and they have proved that warning systems could be developed on the base of a proper prediction model. However, the prediction models and the imperative warning systems are heavily dependent on the instructional design and thereby the prediction models should constantly be verified in terms of their accuracy and sensitivity on the ground that a change in the instructional design or even an emerged risk factor could affect a prediction model’s accuracy. That holds true given that prediction models are based on risk factors and risk factors vary among courses and therefore the probability of an emerged risk factor could not be ruled out in the context of the course’s delivery process in a next cohort. Hence, it is not easy to develop a prediction model for students at risk which is suitable for all cohorts. It is easier to develop a risk model to identify the risk factors of students’ failure in terms of a specific course rather than developing a risk model and an imperative prediction model for all cohorts. Nevertheless, given that instructional design is mot modified through cohorts, the probability of similar risk factors among cohorts will be significant. Although similar risk factors are identified, the risk models should also be verified before being viewed as a pillar on which a prediction model could be generated.
It is also essential to stress on the fact that the prediction models referred in the studies [
18,
19,
20,
21,
22,
23,
24,
25], have not been tested in terms of a blended course. Thereby, there are not significant findings in regard to the students’ critical performance prediction in blended courses which are reported in literature. Nevertheless, some studies [
18,
19] have proved that Moodle LMS data assume an important role into the students’ critical performance and that finding should be considered when attempting to develop a risk model for students at risk in blended courses.